Les troubles neurologiques sont responsables du taux d’invalidité le plus élevé et du deuxième taux de mortalité le plus élevé au monde (Feigin et al., 2020). L'imagerie médicale en neurologie repose principalement sur des modalités qui génèrent de grandes quantités de données complexes, notamment l'imagerie par résonance magnétique (IRM), la tomodensitométrie (TDM) et l'imagerie nucléaire. Ainsi, de nombreuses recherches sur les applications de l’intelligence artificielle (IA) en radiologie ont ciblé les troubles neurologiques. En fait, entre 29 % et 38 % de toutes les applications commerciales basées sur l’IA en radiologie se concentrent sur le cerveau ou la colonne vertébrale, une proportion plus élevée que pour toute autre région anatomique.

La plupart de ces applications visent à aider les radiologues soit en soutenant leur interprétation des images, par exemple en rendant ces tâches plus efficaces, soit en étendant les capacités des radiologues, par exemple en fournissant une quantification plus détaillée des données de neuro-imagerie (Olthof et al., 2020). Ce livre présente les applications les plus courantes de l’IA en neuroradiologie et discute des preuves qui les sous-tendent.

Hémorragie intracrânienne

L’hémorragie intracrânienne (HIC) aiguë touche environ 3,4 millions de personnes chaque année dans le monde (organisation mondiale de l’AVC 2022). L'HIC entraîne une morbidité et une mortalité élevées et nécessite souvent une intervention neurochirurgicale rapide ou un suivi clinique et d'imagerie étroit (Broderick et al., 2007 ; van Asch et al., 2010). En particulier chez les patients présentant des déficits neurologiques aigus et suspectés d'avoir subi un accident vasculaire cérébral, la détection d'une hémorragie intracrânienne aiguë est d'une importance primordiale car il s'agit d'une contre-indication absolue à la thrombolyse intraveineuse (Fugate & Rabinstein, 2015).

En situation d’urgence, les cas suspects d’HIC sont généralement initialement étudiés à l’aide d’une tomodensitométrie sans contraste (NCCT) de la tête. En effet, la tomodensitométrie est largement disponible, rapide, très sensible pour l'HIC et présente relativement peu de contre-indications (A. Jain et al., 2021). L'alternative est l'IRM, qui est plus sensible aux hémorragies très petites et chroniques, mais qui est plus lente, moins facilement disponible, plus coûteuse et contre-indiquée chez certains patients (Chalela et al., 2007).

Dans une étude visant à déterminer les types d'erreurs commises par les internes en radiologie dans la détection de l'HIC, les chercheurs ont trouvé des écarts dans 4,6 % des examens de nuit interprétés par les internes et sur ce pourcentage, 13,6 % étaient dus à une hémorragie qui n'était pas incluse ou rapportée de manière inexacte dans les rapports des internes. (Strub et al., 2007). L'HIC peut être subdivisée en hémorragie intraparenchymateuse, hémorragie intraventriculaire, hémorragie sous-durale, hémorragie extradurale et hémorragie sous-arachnoïdienne. Parmi celles-ci, les hémorragies sous-durales et sous-arachnoïdiennes sont celles qui passent le plus souvent inaperçues, en particulier si elles sont très petites (Strub et al., 2007). De plus, l'anatomie cérébrale normale et les artefacts d'images sont souvent confondus avec une hémorragie intracrânienne par les internes en radiologie (Erly et al., 2002).

La grande majorité des applications basées sur l’IA visant à détecter et à classer les hémorragies intracrâniennes utilisent la NCCT comme entrée et sont basées sur des réseaux neuronaux convolutifs. À quelques exceptions près (Bar et al., 2019 ; Wang et al., 2021 ; Ye et al., 2019), des descriptions très détaillées de l'architecture réseau ne sont pas facilement disponibles pour la plupart des applications. La quantité et la qualité des données utilisées pour entraîner ces algorithmes varient considérablement, allant de centaines (Bar et al., 2019 ; Heit et al., 2021) à des milliers (McLouth et al., 2021 ; Rava, Seymour, et al., 2021) à des dizaines de milliers (Chilamkurthy et al., 2018 ; Gibson et al., 2022 ; Ginat, 2021) d'examens NCCT.

En plus de la classification de la présence ou de l'absence d’HIC, des applications d'algorithmes basés sur l'IA ont également été utilisées pour classer les sous-types d’HIC (Chilamkurthy et al., 2018 ; Gibson et al., 2022 ; Wang et al., 2021 ; Ye et al., 2019), détecter les résultats associés tels que l'effet de masse, le déplacement de la ligne médiane et les fractures (Chilamkurthy et al., 2018), et effectuer la segmentation et la volumétrie des hémorragies (Bar et al., 2019 ; Gibson et al., 2022 ; Heit et al., 2021). De plus, une application basée sur l'IA estime également le degré d'incertitude dans la décision de l'algorithme pour aider le radiologue à interpréter le résultat de l'algorithme (Gibson et al., 2022).

Parmi les sous-types d'HIC, les applications basées sur l'IA issues des études mentionnées montrent généralement la sensibilité la plus élevée à l'hémorragie intraventriculaire (Chilamkurthy et al., 2018 ; Gibson et al., 2022 ; McLouth et al., 2021 ; Wang et al., 2021), probablement en raison de la grande différence de densité TDM entre le liquide céphalo-rachidien et le sang. Dans toutes les applications, la sensibilité est relativement faible pour les hémorragies sous-arachnoïdiennes (Gibson et al., 2022 ; McLouth et al., 2021 ; Rava, Seymour, et al., 2021 ; Wang et al., 2021 ; Ye et al., 2019), peut-être parce que celles-ci ont tendance à être petites et/ou adjacentes à des structures osseuses ou à des artefacts tomodensitométriques hyperdenses (par exemple dans les citernes basales). D'autres applications ont également montré une sensibilité relativement faible à l'hémorragie sous-durale, en particulier dans des endroits moins courants tels que le long de la faux cérébrale (Chilamkurthy et al., 2018 ; Rao et al., 2021 ; Wang et al., 2021 ; Ye et al. , 2019). La sensibilité a également tendance à être plus faible pour les hémorragies plus petites, définies comme < 1,5 ml ou < 5 ml, selon l'étude (Heit et al., 2021 ; McLouth et al., 2021 ; Rava, Seymour, et al., 2021). Une seule des études mentionnées a systématiquement étudié les différences entre les fournisseurs de scanners et les paramètres d'analyse sur les performances de diagnostic des applications basées sur l'IA pour la détection de l’HIC (McLouth et al., 2021).

Certaines études ont directement comparé les performances des applications basées sur l'IA à celles des experts. Dans une étude portant sur 160 NCCT (49 % atteints d'HIC) utilisant l'évaluation d'un consultant en neuroradiologie comme vérité du terrain, un réseau neuronal convolutif (CNN) U-Net a montré une sensibilité (91 %) et une spécificité (89 %) inférieures à celles de deux internes en neuroradiologie (sensibilité de 99 à 100 % et spécificité de 98 %) (Schmitt et al., 2022). Dans une autre étude, les interprétations d’une application basée sur l’IA approuvée par la FDA et marquée CE ont été comparées aux lectures d’un panel de trois neuroradiologues traitants qui ont défini la vérité du terrain.

L'application basée sur l'IA a démontré la même sensibilité qu'un chercheur en neuroradiologie (91,9 %), mais la spécificité de l'application était nettement inférieure (application : 84,4 % ; chercheur : 99,6 %) (Eldaya et al., 2022). Une autre application basée sur l’IA présentait une sensibilité plus élevée et une spécificité légèrement inférieure pour l’HIC que les internes en radiologie (Ye et al., 2019). L’épaississement dural, les calcifications durales et intraparenchymateuses ainsi que les artefacts de mouvement ou de stries sont les plus susceptibles d’être confondus avec l’HIC par les applications basées sur l’IA (Bar et al., 2019 ; Eldaya et al., 2022 ; Rao et al., 2021).

De nombreuses études ont étudié la précision du diagnostic des applications basées sur l'IA pour détecter l'HIC, mais un autre avantage potentiel du dépistage de l'HIC basé sur l'IA est que les examens peuvent être interprétés plus rapidement, ce qui peut conduire à une prise en charge plus rapide des patients. Bien que moins d’études aient évalué l’impact du dépistage basé sur l’IA sur le timing, certaines études soutiennent des temps de lecture plus rapides. Dans une étude portant sur 620 NCCT, le temps écoulé entre la fin de l'examen et le rapport était de 73 minutes lorsque l'IA informait le lecteur humain qu'elle avait trouvé quelque chose, contre 132 minutes lorsqu'aucune notification de ce type n'avait lieu (Wismüller & Stockmaster, 2020). Dans une autre étude, l’utilisation de l’application basée sur l’IA était associée à des séjours plus courts des patients aux urgences (561 minutes contre 781 minutes sans l’IA) (Chien et al., 2022).

AVC ischémique aigu

Occlusion d'un gros vaisseau

Chez les patients ayant subi un accident vasculaire cérébral ischémique aigu, l’identification rapide des occlusions des gros vaisseaux cérébraux est essentielle pour un traitement rapide. En général, le terme « occlusion des gros vaisseaux (OGV) » fait référence aux occlusions d'artères suffisamment grandes pour se prêter à une thrombectomie mécanique. Actuellement, cela inclut l'artère carotide interne (ACI), les parties proximales des artères cérébrales moyennes (M1 et M2), antérieures (A1) et postérieures (P1), ainsi que l'artère basilaire (Mokin et al., 2019 ; Pirson et al., 2022).

Les OGV sont détectés soit directement par angiographie numérique par soustraction, angiographie par TDM ou angiographie par IRM, soit indirectement par des techniques non angiographiques. En angiographie, les occlusions vasculaires apparaissent comme une interruption soudaine du remplissage par contraste d'une artère (dans l'angiographie avec injection de contraste) ou du signal de flux (dans les techniques sans contraste telles que l'angiographie par IRM à temps de vol). Cela peut se produire avec ou sans la présence d’un remplissage de contraste ou d’un signal de flux distal par rapport au site de l’occlusion. Les signes d'imagerie indirecte de l’OGV sur les techniques non angiographiques incluent un vaisseau hyperdense sur NCCT (représentant le thrombus occlusif) (Gács et al., 1983) et un signe de thrombus de susceptibilité sur les images T2* ou IRM pondérées en fonction de la susceptibilité (Flacke et al., 2000).

La plupart des solutions basées sur l'IA pour la détection des OGV utilisent l'angiographie par TDM (Amukotuwa et al., 2019 ; Murray et al., 2020 ; Rava, Peterson, et al., 2021 ; Wardlaw et al., 2022 ; Yahav- Dovrat et al., 2021), tandis que d'autres utilisent le NCCT (Lisowska et al., 2017 ; Olive-Gadea et al., 2020).

La plupart des applications se sont concentrées sur les OGV des artères intracrâniennes de la circulation antérieure (Adhya et al., 2021 ; Amukotuwa et al., 2019 ; Dehkharghani et al., 2021 ; Rava, Peterson, et al., 2021), reflétant le fait que la thrombectomie mécanique est beaucoup moins fréquemment réalisée dans les occlusions des vaisseaux de circulation postérieure (Adusumilli et al., 2022).

Dans une revue des données probantes sur les applications basées sur l'IA pour la détection de l'OGV, les sensibilités étaient comprises entre 80 et 96 % et les spécificités entre 90 et 98 % (Wardlaw et al., 2022). Les faux positifs des études incluses dans l'examen des preuves étaient généralement dus à une sténose artérielle, une hémorragie intracrânienne, des tumeurs hypervasculaires ou des occlusions de vaisseaux distaux qui ne remplissaient pas les critères d'une OGV (Amukotuwa et al., 2019 ; Yahav- Dovrat et al., 2021). Malheureusement, les données de performances publiées ne sont pas disponibles pour un certain nombre de solutions basées sur l'IA marquées CE, y compris certaines conçues pour la détection de l’OGV (van Leeuwen et al., 2021).

Au moment de la rédaction de cette publication, il n’existe qu’une seule étude portant sur la rentabilité des outils basés sur l’IA pour la détection de l’OGV. L'analyse de l'étude a montré que, en supposant que 6 % des OGV sont manqués par les cliniciens et que l'IA peut aider à réduire ce chiffre de moitié, des économies de coûts de 11 millions de dollars par an pourraient être réalisées au Royaume-Uni (van Leeuwen, Meijer, et al., 2021).

Étant donné que les radiologues et les internes en radiologie ne manquent pas souvent les OGV avec des techniques angiographiques (Duvekot et al., 2021), le principal avantage potentiel de la détection des OGV basée sur l'IA est d'accélérer le traitement en fournissant une évaluation plus rapide. Certaines des applications actuellement disponibles nécessitent entre 1 et 3,5 minutes environ pour traiter les données et prendre une décision concernant la présence d'une OGV (Amukotuwa et al., 2019 ; Dehkharghani et al., 2021 ; Olive-Gadea et al., 2020). Certains outils ont été associés à une réduction d'environ 22,5 minutes du délai entre l'imagerie et le transfert du patient vers un hôpital capable de réaliser une thrombectomie mécanique (Hassan et al., 2020), le temps écoulé entre l'arrivée du patient à l'hôpital et la notification du l'équipe neuroendovasculaire d'environ 15 minutes (Morey et al., 2021), et le temps entre l'imagerie et la ponction à l'aine pour une thrombectomie mécanique d'environ 25 minutes (Adhya et al., 2021).

Modifications ischémiques précoces du tissu cérébral

Sur la TDM, les modifications précoces du tissu cérébral associées à l'ischémie comprennent un gonflement des tissus et une atténuation réduite des tissus due à un oedème ionique (Marks et al., 1999). Ces changements sont intégrés aux outils d’évaluation visuelle utilisés par les radiologues, le plus courant étant l’Alberta Stroke Program Early CT Score (ASPECTS). ASPECTS peut aider à prédire à la fois les résultats fonctionnels et le développement d'une hémorragie intracrânienne symptomatique après une thrombolyse intraveineuse (Schröder et Thomalla, 2016). La plupart des applications basées sur l'IA visant à détecter les changements ischémiques précoces sur NCCT le font en fournissant une évaluation automatisée des ASPECTS (Wardlaw et al., 2022). D'autres applications visent à identifier les changements ischémiques précoces par angiographie TDM (Abdelkhaleq et al., 2021 ; Öman et al., 2019) ou perfusion TDM (Hakim et al., 2021). 2021).

La majorité des algorithmes basés sur l'IA pour identifier les changements ischémiques précoces sur la tomodensitométrie ont utilisé l'évaluation visuelle de la NCCT par des radiologues, des neuroradiologues ou d'autres cliniciens comme norme de référence (Goebel et al., 2018 ; Hoelter et al., 2020 ; Kniep et al., 2020 ; Maegerlein et al., 2019 ; Seker et al., 2019), tandis que certains ont utilisé l'imagerie IRM pondérée en diffusion (Abdelkhaleq et al., 2021 ; Herweh et al., 2016 ; H. Kuang et al., 2019 ; Qiu et al., 2020) ou le noyau de l'infarctus défini par perfusion TDM (Olive- Gadea et al., 2019). La plupart de ces applications utilisent soit des forêts aléatoires (Guberina et al., 2018 ; Herweh et al., 2016 ; Kniep et al., 2020 ; H. Kuang et al., 2019 ; Maegerlein et al., 2019 ; Nagel et al., 2017 ; Olive-Gadea et al., 2019 ; Qiu et al., 2020) soit des réseaux de neurones convolutifs (Öman et al., 2019). De plus, de nombreuses études se sont concentrées sur l'identification automatisée des modifications ischémiques précoces sur l'IRM pondérée en diffusion (Boldsen et al., 2018 ; Mohd Saad et al., 2019 ; Nazari- Farsani et al., 2020 ; Siddique et al., 2022 ; Song, 2019 ; Wong et al., 2022), qui est une méthode très sensible mais peu disponible dans les contextes aigus.

À l’instar des applications OGV, les données de performances accessibles au public ne sont pas disponibles pour certaines solutions basées sur l’IA marquées CE pour la détection de changements ischémiques précoces (van Leeuwen et al., 2021). L'algorithme pour lequel les données les plus publiées sont disponibles est une approche de forêts aléatoires pour l'évaluation ASPECTS qui s'est révélée non inférieure aux neuroradiologues avec une sensibilité de 44 % et une spécificité de 93 % en utilisant la tomodensitométrie de suivi comme vérité du terrain (Nagel et al., 2017). Une autre étude utilisant le même algorithme et la même méthode de vérification sur le terrain a révélé que l'algorithme avait une sensibilité plus élevée (83 % contre 73 %) mais une spécificité plus faible (57 % contre 84 %) pour la notation ASPECTS par rapport aux neuroradiologues (Guberina et al., 2018). Dans une troisième étude, cet algorithme a également obtenu de meilleurs résultats en matière de notation ASPECTS par rapport aux neurologues et aux internes en neurologie, et a obtenu des résultats similaires par rapport aux neuroradiologues (Ferreti et al., 2020).

Dans l’ensemble, peu d’études ont comparé directement différentes applications basées sur l’IA pour détecter les changements ischémiques précoces sur NCCT (Goebel et al., 2018 ; Hoelter et al., 2020). Dans une étude, trois applications disponibles dans le commerce (deux basées sur l’apprentissage automatique et une basée sur la densitométrie) ont été comparées chez 131 patients (Hoelter et al., 2020).

L’étude a révélé que les applications basées sur l’IA avaient une aire sous la courbe (ASC) comprise entre 0,73 et 0,76 par rapport au consensus de trois neuroradiologues.

L'évaluation visuelle des changements ischémiques précoces sur NCCT est particulièrement difficile dans la fosse postérieure, où les artefacts sont fréquents et entravent l'interprétabilité (Hwang et al., 2012). Dans une cohorte de 69 patients présentant des occlusions de l'artère basilaire et ayant passé une NCCT dans les 6 heures suivant l'apparition des symptômes, un algorithme basé sur une forêt aléatoire a identifié des modifications ischémiques précoces dans la circulation postérieure avec une ASC allant de 0,70 (dans le cervelet) à 0,82 (dans le thalamus) en utilisant la NCCT de suivi comme vérité du terrain (Kniep et al., 2020).

Plusieurs facteurs, outre la localisation anatomique, influencent la détectabilité des modifications ischémiques précoces sur la NCCT. Une étude a révélé que la précision de l'évaluation ASPECTS diffère selon le type de reconstruction TDM utilisé, mais qu'un algorithme automatisé fonctionne de manière plus cohérente sur plusieurs reconstructions TDM étudiées que les internes ou consultants en radiologie (Seker et al., 2019). De plus, la précision des évaluations ASPECTS humaines et basées sur l’IA augmente avec le temps écoulé entre l’apparition des symptômes et la NCCT, en raison des changements ischémiques précoces devenant plus prononcés (Potreck et al., 2022).

AVC dont l’heure d’apparition est inconnue

Savoir combien de temps s'est écoulé depuis l'apparition des symptômes d'un AVC est crucial pour orienter un traitement approprié, car la thrombolyse intraveineuse n'est indiquée que lorsqu'elle est administrée dans les 4,5 heures suivant l'apparition des symptômes (Powers et al., 2018). Le début de l’AVC n’est pas toujours définitif, par exemple chez les patients présentant un AVC au réveil. Un accident vasculaire cérébral au réveil survient chez environ 14 % des patients, comme le révèle une étude basée sur la population réalisée sur des patients se présentant aux urgences (Mackey et al., 2011). Plusieurs approches basées sur l'imagerie pour identifier les patients dans la fenêtre temporelle de thrombolyse ont été proposées.

Une approche minutieusement étudiée jusqu'à présent a été la présence d'une lésion aiguë d'accident vasculaire cérébral sur l'imagerie pondérée en diffusion (DWI) et son absence sur l'IRM de récupération par inversion atténuée par fluide (FLAIR). (Ebinger et al., 2010 ; Thomalla et al., 2011 ; Thomalla et al., 2018). L’interprétation automatisée des images IRM DWI et FLAIR est également devenue la cible des algorithmes basés sur l’IA conçus pour aider les radiologues.

Les approches de classification basée sur l'IA des délais d'apparition des accidents vasculaires cérébraux ont inclus les CNN (Polson et al., 2022) ou une combinaison de différents algorithmes d'apprentissage automatique (Jiang et al., 2022 ; H. Lee et al., 2020 ; Zhu et al., 2021). Certaines études ont utilisé une approche basée sur la radiomique impliquant la segmentation des lésions DWI et FLAIR, en extrayant différentes caractéristiques d'imagerie, puis en transmettant ces caractéristiques à différents algorithmes de classification (Jiang et al., 2022 ; H. Lee et al., 2020 ; Zhu et al., 2021).

La classification basée sur l'IA des délais d'apparition des accidents vasculaires cérébraux a généré des sensibilités plus élevées mais des spécificités plus faibles que l'évaluation visuelle réalisée par les radiologues dans plusieurs études (H. Lee et al., 2020 ; Polson et al., 2022). Des sensibilités allant de 73 à 86 % et des spécificités allant de 68 à 85 % ont été rapportées (Jiang et al., 2022 ; H. Lee et al., 2020 ; Polson et al., 2022 ; Zhu et al., 2021). Une étude utilisant une approche radiomique basée uniquement sur les images pondérées DWI et T1 combinées à un algorithme d'apprentissage profond a trouvé une sensibilité de 95 % et une spécificité de 50 % pour l'identification des patients dans la fenêtre temporelle de thrombolyse (Y.-Q. Zhang et al., 2022).

Lésion cérébrale traumatique

Un traumatisme craniocérébral (TCC) aigu est un traumatisme physique soudain qui endommage le cerveau. Ses manifestations comprennent l'HIC, les lésions axonales diffuses et les fractures du crâne et du visage. De plus, les conséquences de certaines de ces manifestations, telles que le déplacement de la ligne médiane et la hernie cérébrale, qui peuvent nécessiter un traitement d'urgence en cas de gravité, peuvent être détectées par imagerie (Schweitzer et al., 2019).

Bien que les fractures du crâne non déplacées sans HIC associée soient traitées de manière conservatrice (Skull Fractures, n.d.), peu d'études ont abordé leur détection à l'aide de techniques basées sur l'IA. Néanmoins, certaines tentatives ont été faites récemment pour classer les fractures du crâne détectées sur la NCCT.

Un algorithme basé sur une approche d'apprentissage multi-label et entraîné sur 174 NCCT (dont 103 avec fractures) a montré une précision de 98 % et une spécificité de 92 % pour la détection des fractures du crâne (Emon et al., 2022). Les précisions et spécificités les plus faibles concernaient les fractures déprimées, et les précisions et spécificités les plus élevées concernaient les fractures linéaires et les fractures faciales. Une application basée sur l'apprentissage profond visant à détecter les résultats critiques sur la tomodensitométrie de la tête sans contraste a montré une sensibilité de 81,2 à 87,2 % et une spécificité de 77,5 à 86,1 % (en fonction de l'ensemble de données de test) pour détecter les fractures du crâne (Chilamkurthy et al., 2018). Dans la même étude, le déplacement de la ligne médiane et l'effet de masse, deux conséquences courantes de l'HIC lié à un traumatisme, ont été identifiés avec une sensibilité de 87,5 à 90,1 % et de 70,9 à 81,2 % ainsi qu'une spécificité de 83,7 à 89,4 % et de 61,6 à 73,4 % (en fonction de l'ensemble de données de test), respectivement. Un algorithme combinant l'extraction des caractéristiques morphologiques du crâne avec les CNN et qui a été entraîné sur 25 NCCT et testé sur 10 NCCT provenant de patients traumatisés crâniens avait une précision moyenne de 60 % pour la détection des fractures du crâne (Z. Kuang et al., 2020). Un autre algorithme d'apprentissage profond était sensible à 91,4 % et spécifique à 87,5 % dans l'identification des fractures du crâne dans une série de 150 TDM de la tête post-mortem (Heimer et al., 2018).

Maladies neurodégénératives

De nombreuses affections neurologiques peuvent être décrites comme neurodégénératives, mais le terme est généralement utilisé pour désigner des maladies neurologiques chroniques associées à une perte progressive du tissu cérébral et provoquant généralement une démence et/ou un dysfonctionnement moteur (Lamptey et al., 2022). Plus d'un cinquième des algorithmes basés sur l'IA certifiés CE ou approuvés par la FDA en neuroradiologie ciblent les patients atteints de démence. La plupart d'entre eux calculent automatiquement les volumes cérébraux régionaux, mesurent l'épaisseur corticale et quantifient les lésions de la substance blanche causées par une maladie des petits vaisseaux cérébraux.

De nombreux algorithmes basés sur l'IA spécifiques à une maladie ciblent la maladie d'Alzheimer (MA), qui est caractérisée pathologiquement par des plaques extracellulaires composées de β-amyloïde et d'enchevêtrements neurofibrillaires intracellulaires contenant de la protéine Tau et qui entraîne des troubles cognitifs amnésiques et non amnésiques progressifs (Knopman et al., 2021). Certains de ces algorithmes sont capables de distinguer entre la MA et les individus cognitivement normaux à l'aide de l'IRM avec des sensibilités allant de 78 à 99,1 % et des spécificités allant de 70 à 92,68 % (Battineni et al., 2022). Une approche basée sur des machines à vecteurs de support non linéaires a permis de différencier la MA des autres syndromes de démence comme la dégénérescence lobaire fronto-temporale avec une précision de 84 % (Davatzikos et al., 2008).

Des efforts ont également été déployés pour prédire la conversion de la phase prodromique de la MA en MA clinique, car on pense que c'est dans la première phase que les interventions thérapeutiques pourraient être particulièrement efficaces (Crous-Bou et al., 2017).

La déficience cognitive légère (DCL) décrit un état dans lequel les individus ont des déficits cognitifs plus sévères que ce qui est attendu pour leur âge, mais qui n'interfèrent pas de manière significative avec leurs activités quotidiennes (Petersen, 2016). Plusieurs approches basées sur l'IA ont été utilisées pour prédire la conversion de la DCL en MA avec des précisions de 66 à 92 % (Amoroso et al., 2018 ; Bron et al., 2015 ; Lebedev et al., 2014 ; G. Lee et al., 2019 ; Lu et al., 2018 ; Moradi et al., 2015 ; Ocasio et Duong, 2021 ; Salvatore et al., 2015 ; Spasov et al., 2019).

Un diagnostic précoce est également considéré comme important pour le traitement efficace de la maladie de Parkinson (MP) (Pagan, 2012), une autre maladie neurodégénérative courante caractérisée pathologiquement par une dégénérescence des neurones dopaminergiques de la substantia nigra. Au moment où les symptômes moteurs qui orientent vers un diagnostic clinique de MP apparaissent, on estime que plus de 60 % des neurones dopaminergiques du cerveau ont été perdus (GBD 2016 Parkinson's Disease Collaborators, 2018). Plusieurs approches d'apprentissage automatique ont été développées pour distinguer la MP des témoins sains à l'aide de caractéristiques morphologiques dérivées de l'IRM structurelle (Adeli et al., 2016 ; Chakraborty et al., 2020 ; Peng et al., 2017), de l'IRM fonctionnelle (Long et al., 2012 ; Pläschke et al., 2017 ; Tang et al., 2017), de la tomographie par émission de positons (TEP) (Piccardo et al., 2021) et de la tomographie par émission monophotonique (SPECT) (Choi et al., 2017 ; Hirschauer et al., 2015 ; Ozsahin et al., 2020), souvent en combinaison avec des scores cliniques.

Étant donné que les symptômes moteurs de la MP se chevauchent avec ceux d’autres affections neurologiques, les caractéristiques cliniques à elles seules ne suffisent souvent pas pour diagnostiquer avec confiance la MP (Rizzo et al., 2016). Distinguer la MP idiopathique des syndromes parkinsoniens atypiques tels que l'atrophie multisystémique et la paralysie supranucléaire progressive sur la base des caractéristiques cliniques est particulièrement difficile (Rizzo et al., 2016). Tirant parti du potentiel de la neuro-imagerie pour aider à faire cette distinction, une première étude a utilisé l'apprentissage automatique de vecteurs de support pour classer la MP idiopathique et d'autres causes de parkinsonisme à l'aide de l'imagerie du tenseur de diffusion avec une sensibilité de 94 % et une spécificité de 100 % (Haller et al., 2012). Plusieurs autres études ont montré une grande précision dans la distinction entre la MP idiopathique et le parkinsonisme atypique à l'aide de l'IRM structurelle (Duchesne et al., 2009 ; Focke et al., 2011 ; Huppertz et al., 2016 ; Marquand et al., 2013 ; Salvatore et al., 2014), l'imagerie pondérée en fonction de la susceptibilité (Haller et al., 2013) et une combinaison d'imagerie du tenseur de diffusion et d'IRM structurelle (Cherubini et al., 2014).

Des études ont également été réalisées à l’aide de modèles d’apprentissage automatique pour aider à orienter le traitement de la MP. Une étude portant sur 67 patients atteints de MP a révélé que les caractéristiques extraites de l'IRM fonctionnelle peuvent classer les paramètres optimaux et sous-optimaux pour la stimulation cérébrale profonde avec une précision de 88 % (Boutet et al., 2021). Cela pourrait aider à optimiser le processus actuellement long, coûteux et fastidieux des tests cliniques approfondis nécessaires pour optimiser les paramètres de stimulation cérébrale profonde chez les patients parkinsoniens.

Sclérose en plaques

La sclérose en plaques (SEP) est une maladie auto-immune courante du système nerveux central caractérisée pathologiquement par une démyélinisation inflammatoire et conduisant à un large éventail de manifestations neurologiques (McGinley et al., 2021). L'IRM joue un rôle important dans le diagnostic et la prise en charge de la SEP et constitue la technique d'imagerie de choix pour quantifier et classer les lésions de SEP dans le cerveau et la moelle épinière (Matthews et al., 2016). Les caractéristiques d'imagerie constituent un élément crucial des critères de diagnostic de la SEP (Thompson et al., 2018) et les directives recommandent d'utiliser l'IRM pour surveiller les patients et orienter le traitement (Wattjes et al., 2015). Plusieurs algorithmes basés sur l'IA ont reçu l'autorisation de la FDA et la certification CE pour la quantification de l'atrophie cérébrale et la segmentation automatisée des lésions dans la SEP (Cavedo et al., 2022 ; Qubiotech Neurocloud Vol, 2021 ; Zaki et al., 2022).

De nombreux algorithmes basés sur l'IA dans la SEP se concentrent sur l'extraction automatisée des caractéristiques d'imagerie (Afzal et al., 2022 ; Bonacchi et al., 2022 ; Eichinger et al., 2020 ; Moazami et al., 2021). L'évaluation visuelle de la présence de lésions de SEP et de leur progression dans le temps constitue une partie importante du diagnostic et de la surveillance de la SEP, mais elle prend du temps et est difficile (Danelakis et al., 2018). Au lieu de cela, plusieurs apprentissages automatiques traditionnels (Brosch et al., 2016 ; Goldberg-Zimring et al., 1998 ; Karimian et Jafari, 2015 ; Samarasekera et al., 1997 ; Schmidt et al., 2012 ; S. Zhang et al., 2018) et des approches d'apprentissage profond (Birenbaum & Greenspan, 2017 ; Deshpande et al., 2015 ; Roy et al., 2018 ; Valverde et al., 2017, 2019) pour segmenter automatiquement les lésions de SEP ont été développées. Environ 30 % de ces études utilisent des CNN et 40 % utilisent des approches d'apprentissage automatique à vecteurs de support (Afzal et al., 2022).

Les approches d'apprentissage profond ont généré des coefficients de similarité Dice (une mesure du chevauchement spatial allant de 0 à 1) de 0,52 à 0,67 par rapport aux segmentations manuelles des lésions (Afzal et al., 2022). Plusieurs approches basées sur l'IA pour quantifier automatiquement l'atrophie cérébrale, qui est un autre prédicteur d'imagerie de l'évolution de la SEP (Andravizou et al., 2019), ont également été étudiées (Dolz et al., 2018 ; Kushibar et al., 2018 ; Wachinger et al., 2018).

Des algorithmes basés sur l’IA ont également été exploités pour identifier les anomalies de l’IRM qui ne sont pas clairement visibles à l’oeil nu et ne sont pas incluses dans les critères diagnostiques actuels de la SEP. Il s'agit notamment d'anomalies des veines cérébrales et de dépôts de fer détectés à l'aide de l'imagerie pondérée par la susceptibilité (Lopatina et al., 2020) et d'anomalies dans des zones d'apparence normale de la substance blanche et grise dans des séquences d'IRM conventionnelles (Eitel et al., 2019) et avancées (Neeb & Schenk, 2019 ; Saccà et al., 2019 ; Yoo et al., 2018 ; Zurita et al., 2018).

L'exclusion des maladies avec une présentation clinique similaire est nécessaire au diagnostic de la SEP, mais s'avère parfois difficile (Wildner et al., 2020). En utilisant les caractéristiques extraites de l'IRM, les forêts aléatoires et les CNN ont permis de distinguer avec précision la SEP des troubles du spectre de la neuromyélite optique (Eshaghi et al., 2016 ; Rocca et al., 2021), les troubles non inflammatoires de la substance blanche (Mangeat et al., 2020 ; Theocharakis et al., 2009), la migraine (Rocca et al., 2021), la vascularite du système nerveux central (Rocca et al., 2021) et les tumeurs cérébrales (Ekşi et al., 2021).

La SEP est divisée en plusieurs phénotypes cliniques qui ont des pronostics différents et des stratégies de traitement optimales (Lublin et al., 2014). À l'aide de l'IRM du tenseur de diffusion (Kocevar et al., 2016 ; Marzullo et al., 2019), de la spectroscopie par résonance magnétique (EkŞİ et al., 2020 ; Ion- Mărgineanu et al., 2017) et des mesures d'atrophie basées sur l'IRM (Bonacchi et al., 2020), plusieurs études ont étudié le potentiel des approches basées sur l'IA conçues pour distinguer les différents phénotypes cliniques de la SEP.

Le traitement de la SEP est personnalisé en fonction de marqueurs pronostiques cliniques, démographiques, de laboratoire et d'imagerie (Rotstein et Montalban, 2019). Plusieurs algorithmes basés sur l'IA ont été évalués pour leur capacité à prédire la conversion du premier épisode clinique évocateur d'une maladie inflammatoire chronique du SNC, connue sous le nom de « syndrome cliniquement isolé », à la SEP définie à l'aide des caractéristiques de l'IRM avec des sensibilités de 64 à 77 % et spécificités de 66 à 78 % (Bendfeldt et al., 2019 ; Wottschel et al., 2015, 2019). Des algorithmes basés sur l'IA combinant des données cliniques et IRM ont également été conçus pour prédire l'évolution de la maladie et le handicap clinique (Filippi et al., 2013 ; Roca et al., 2020 ; Tommasin et al., 2021 ; Zhao et al., 2017, 2020). À l'aide de machines à vecteurs de support et d'arbres extrêmement randomisés, une étude a révélé qu'une « empreinte digitale » d'imagerie de grande dimension dérivée d'images pondérées en T1 et de FLAIR était plus efficace pour prédire la réponse au traitement de la SEP que les mesures de la réponse au traitement dérivées de l'IRM conventionnelle, telles que le volume cérébral et le nombre et le volume des lésions (ASC 0,89 vs. 0,69) (Kanber et al., 2019).

De plus, les algorithmes basés sur l’IA ont montré leur potentiel pour faciliter les protocoles d’IRM utilisés dans la SEP. Cela inclut l’extraction d’informations à partir de séquences IRM conventionnelles, la génération de séquences synthétiques à partir d’images acquises, par exemple des images avec contraste amélioré provenant d’une IRM non améliorée (Bonacchi et al., 2022).

Neuro-oncologie

Les tumeurs cérébrales comprennent à la fois les tumeurs primitives du cerveau (qui peuvent être bénignes ou malignes) et les tumeurs métastatiques provenant d'autres parties du corps. Il est important de faire la distinction entre les tumeurs cérébrales et d’autres affections en imagerie pour éviter les biopsies inutiles et guider la prise en charge (Abd- Ellah et al., 2019). En extrayant les caractéristiques des IRM pondérées en T1 et T2, un classificateur basé sur la régression a atteint une ASC allant jusqu'à 0,99 pour distinguer les gliomes sans rehaussement et les lésions cérébrales inflammatoires (Y. Han et al., 2021). Des approches basées sur l'IA ont également été conçues pour prédire les grades histopathologiques des gliomes sur la base de l'IRM, atteignant une précision moyenne de 89 % ± 0,09 %, rapportée dans une revue systématique de la littérature (Bahar et al., 2022). De plus, un classificateur de forêts aléatoires utilisant les caractéristiques de l’IRM multiparamétrique a mieux réussi que deux radiologues expérimentés à identifier la tumeur primaire en cas de métastases cérébrales (Kniep et al., 2019).

L’un des cas d’utilisation les plus prometteurs de l’IA en imagerie neuro-oncologique concerne l’utilisation de l’IRM pour identifier les mutations génétiques associées aux tumeurs. Ce domaine, connu sous le nom de radiogénomique, est important car des tumeurs histopathologiquement similaires avec des mutations différentes répondent différemment à des stratégies de traitement spécifiques (Singh et al., 2021). La radiogénomique consiste à extraire des caractéristiques de plusieurs séquences d’IRM (séquences anatomiques ainsi que séquences pondérées en diffusion et de perfusion) et à utiliser ces caractéristiques pour prédire les altérations génomiques de la tumeur. Plusieurs approches utilisant des arbres de décision et des forêts aléatoires ont été conçues pour prédire à la fois des mutations génétiques uniques ainsi que des ensembles plus complexes d'altérations génomiques dans les tumeurs cérébrales (Akkus et al., 2017 ; P. Chang et al., 2018 ; L. Han & Kamdar, 2018 ; Hu et al., 2017 ; Kickingereder et al., 2016 ; Park et al., 2020).

L'imagerie joue également un rôle important dans l'évaluation de la réponse des tumeurs cérébrales à des traitements tels que la radiothérapie, l'immunothérapie, la chimiothérapie et la chirurgie. Cela nécessite une expertise importante, en particulier parce que les caractéristiques d'imagerie de la réponse et de la récidive se chevauchent avec d'autres changements liés au traitement, tels que la pseudoprogression, qui est une augmentation transitoire de la prise de contraste et/ou de l'oedème péritumoral après une radiothérapie et une chimiothérapie (Raimbault et al., 2014 ; Thust et al., 2018).

Dans le glioblastome, la tumeur cérébrale primitive maligne la plus courante chez l'adulte, l'évaluation de la réponse au traitement consiste à mesurer manuellement le volume de tissu tumoral qui absorbe l'agent de contraste (Leao et al., 2020). Une segmentation automatisée précise du tissu tumoral cérébral a été obtenue à l'aide de l'apprentissage automatique de vecteurs de support, de forêts aléatoires et de CNN (Havaei et al., 2017 ; Kickingereder et al., 2019 ; Menze et al., 2015), avec certaines applications certifiées CE disponibles (BioMind, n.d.). Une méta-analyse a révélé que l’apprentissage profond surpasse les approches traditionnelles d’apprentissage automatique pour la segmentation des tumeurs (Kouli et al., 2022). Dans une vaste étude multicentrique portant sur des données cliniques, la volumétrie automatisée des tumeurs à l'aide de CNN a montré une supériorité suggérée par rapport à la volumétrie manuelle dans le calcul du délai de progression de la maladie (Kickingereder et al., 2019).

Les approches utilisant la classification basée sur la régression et les CNN ont montré des résultats permettant de distinguer la pseudo-progression de la véritable progression des tumeurs cérébrales en IRM (Jang et al., 2018, 2020 ; J. Y. Kim et al., 2019). Les CNN se sont également révélés prometteurs pour différencier la nécrose radiologique de la progression tumorale, atteignant une sensibilité de 99,4 % et une spécificité de 97,5 % (Q. Zhang et al., 2019). Les marges des tumeurs cérébrales et leur infiltration des tissus environnants peuvent être très difficiles à distinguer de l'oedème péritumoral. Les classificateurs basés sur la régression, les classificateurs d'apprentissage automatique à vecteurs de support et les SNC ont produit des cartes précises de l'infiltration péritumorale qui peuvent s'avérer utiles pour la planification chirurgicale (Akbari et al., 2016 ; P. D. Chang, Chow, et al., 2017 ; P. D. Chang, Malone, et al., 2017).

Il y a eu un intérêt pour les approches basées sur l’IA conçues pour améliorer l’IRM et aider les radiologues à diagnostiquer les tumeurs cérébrales. Certaines cibles de ces approches basées sur l'IA comprennent les algorithmes de reconstruction d'images basés sur les CNN qui améliorent la résolution spatiale et réduisent le bruit, permettant ainsi de visualiser des structures anatomiques et des composants tumoraux plus petits. De telles approches ont montré des résultats pour détecter les microadénomes hypophysaires, identifier les tumeurs résiduelles ou récurrentes après traitement et caractériser l'invasion tumorale (M. Kim et al., 2021 ; D. H. Lee et al., 2021). De plus, les images avec contraste amélioré synthétisées à partir d'images sans contraste à l'aide de réseaux contradictoires génératifs (GAN) se sont révélées utiles pour évaluer la réponse au traitement du glioblastome et peuvent aider à réduire l'utilisation d'agents de contraste IRM (Jayachandran Preetha et al., 2021).

Conclusion

En l’espace d’une décennie environ, la recherche sur les applications de l’IA en neuroradiologie a réalisé des progrès remarquables. L’IA s’est révélée particulièrement utile pour faciliter le diagnostic de pathologies telles que les accidents vasculaires cérébraux et les hémorragies intracrâniennes, pour lesquelles une détection rapide est cruciale. Il existe également de plus en plus de preuves selon lesquelles l’IA pourrait être utilisée pour surveiller la progression de troubles neurologiques, prédire les résultats et, à terme, permettre des stratégies de traitement plus personnalisées et plus efficaces. La recherche sur les algorithmes basés sur l’IA devrait être complétée à l’avenir par l’analyse du rapport coût-efficacité de ces applications et par la mesure de l’effet de leur mise en oeuvre sur les résultats globaux pour les patients. En outre, ces applications devraient être étayées par davantage de données publiées sur leurs performances afin d’encourager leur utilisation. Dans l’ensemble, l’utilisation de l’IA en neuroradiologie est très prometteuse pour améliorer la qualité des soins aux patients.

Références

Abdelkhaleq, R., Kim, Y., Khose, S., Kan, P., Salazar-Marioni, S., Giancardo, L., & Sheth, S. A. (2021). Automated prediction of final infarct volume in patients with large-vessel occlusion acute ischemic stroke. Neurosurgical Focus, 51(1), E13. https://doi.org/10.3171/2021.4.FOCUS21134

Abd-Ellah, M. K., Awad, A. I., Khalaf, A. A. M., & Hamed, H. F. A. (2019). A review on brain tumor diagnosis from MRI images: Practical implications, key achievements, and lessons learned. Magnetic Resonance Imaging, 61, 300–318. https://doi.org/10.1016/j.mri.2019.05.028

Adeli, E., Shi, F., An, L., Wee, C.-Y., Wu, G., Wang, T., & Shen, D. (2016). Joint feature-sample selection and robust diagnosis of Parkinson’s disease from MRI data. NeuroImage, 141, 206–219. https://doi.org/10.1016/j.neuroimage.2016.05.054

Adhya, J., Li, C., Eisenmenger, L., Cerejo, R., Tayal, A., Goldberg, M., & Chang, W. (2021). Positive predictive value and stroke workflow outcomes using automated vessel density (RAPID-CTA) in stroke patients: One year experience. The Neuroradiology Journal, 34(5), 476–481. https://doi.org/10.1177/19714009211012353

Adusumilli, G., Pederson, J. M., Hardy, N., Kallmes, K. M., Hutchison, K., Kobeissi, H., Heiferman, D. M., & Heit, J. J. (2022). Mechanical thrombectomy in anterior vs. posterior circulation stroke: A systematic review and meta-analysis. Interventional Neuroradiology: Journal of Peritherapeutic Neuroradiology, Surgical Procedures and Related Neurosciences, 15910199221100796. https://doi. org/10.1177/15910199221100796

Afzal, H. M. R., Luo, S., Ramadan, S., & Lechner-Scott, J. (2022). The emerging role of artificial intelligence in multiple sclerosis imaging. Multiple Sclerosis, 28(6), 849–858. https://doi.org/10.1177/1352458520966298

AI Central. (n.d.). Retrieved July 2, 2022, fromhttps://aicentral.com/

AI for radiology(n.d.). Retrieved July 2, 2022, from https://grand-challenge.org/aiforradiology/

Akbari, H., Macyszyn, L., Da, X., Bilello, M., Wolf, R. L., Martinez-Lage, M., Biros, G., Alonso-Basanta, M., O’Rourke, D. M., & Davatzikos, C. (2016). Imaging Surrogates of Infiltration Obtained Via Multiparametric Imaging Pattern Analysis Predict Subsequent Location of Recurrence of Glioblastoma. Neurosurgery, 78(4), 572–580. https://doi.org/10.1227/NEU.0000000000001202

Akkus, Z., Ali, I., Sedlář, J., Agrawal, J. P., Parney, I. F., Giannini, C., & Erickson, B. J. (2017). Predicting Deletion of Chromosomal Arms 1p/19q in Low-Grade Gliomas from MR Images Using Machine Intelligence. Journal of Digital Imaging, 30(4), 469–476. https://doi.org/10.1007/s10278-017-9984-3

Amoroso, N., Diacono, D., Fanizzi, A., La Rocca, M., Monaco, A., Lombardi, A., Guaragnella, C., Bellotti, R., Tangaro, S., & Alzheimer’s Disease Neuroimaging Initiative. (2018). Deep learning reveals Alzheimer’s disease onset in MCI subjects: Results from an international challenge. Journal of Neuroscience Methods, 302, 3–9. https://doi.org/10.1016/j. jneumeth.2017.12.011

Amukotuwa, S. A., Straka, M., Smith, H., Chandra, R. V., Dehkharghani, S., Fischbein, N. J., & Bammer, R. (2019). Automated Detection of Intracranial Large Vessel Occlusions on Computed Tomography Angiography: A Single Center Experience. Stroke; a Journal of Cerebral Circulation, 50(10), 2790–2798. https://doi.org/10.1161/STROKEAHA.119.026259

Andravizou, A., Dardiotis, E., Artemiadis, A., Sokratous, M., Siokas, V., Tsouris, Z., Aloizou, A.-M., Nikolaidis, I., Bakirtzis, C., Tsivgoulis, G., Deretzi, G., Grigoriadis, N., Bogdanos, D. P., & Hadjigeorgiou, G. M. (2019). Brain atrophy in multiple sclerosis: mechanisms, clinical relevance and treatment options. Auto- Immunity Highlights, 10(1), 7. https://doi.org/10.1186/ s13317-019-0117-5

Bahar, R. C., Merkaj, S., Cassinelli Petersen, G. I., Tillmanns, N., Subramanian, H., Brim, W. R., Zeevi, T., Staib, L., Kazarian, E., Lin, M., Bousabarah, K., Huttner, A. J., Pala, A., Payabvash, S., Ivanidze, J., Cui, J., Malhotra, A., & Aboian, M. S. (2022). Machine Learning Models for Classifying Highand Low-Grade Gliomas: A Systematic Review and Quality of Reporting Analysis. Frontiers in Oncology, 12, 856231. https://doi.org/10.3389/fonc.2022.856231

Bar, A., Mauda, M., Turner, Y., Safadi, M., & Elnekave, E. (2019). Improved ICH classification using task-dependent learning. In arXiv [cs.CV]. arXiv. https://uploads-ssl.webflow. com/602a32732226833dce680ffe/61733c14eabf6d96471c5 2b4_7-Bar_bloodNet__Improved_ICH_classification_using_task_ dependent_learning__isbi2018.pdf

Battineni, G., Chintalapudi, N., Hossain, M. A., Losco, G., Ruocco, C., Sagaro, G. G., Traini, E., Nittari, G., & Amenta, F. (2022). Artificial Intelligence Models in the Diagnosis of Adult-Onset Dementia Disorders: A Review. Bioengineering (Basel, Switzerland), 9(8). https://doi.org/10.3390/ bioengineering9080370

Bendfeldt, K., Taschler, B., Gaetano, L., Madoerin, P., Kuster, P., Mueller-Lenke, N., Amann, M., Vrenken, H., Wottschel, V., Barkhof, F., Borgwardt, S., Klöppel, S., Wicklein, E.- M., Kappos, L., Edan, G., Freedman, M. S., Montalbán, X., Hartung, H.-P., Pohl, C., … Nichols, T. E. (2019). MRI-based prediction of conversion from clinically isolated syndrome to clinically definite multiple sclerosis using SVM and lesion geometry. Brain Imaging and Behavior, 13(5), 1361–1374. https://doi.org/10.1007/s11682-018-9942-9

BioMind. (n.d.). Retrieved December 20, 2022, from https://biomind.ai/paper

Birenbaum, A., & Greenspan, H. (2017). Multi-view longitudinal CNN for multiple sclerosis lesion segmentation. Engineering Applications of Artificial Intelligence, 65, 111–118. https://doi.org/10.1016/j.engappai.2017.06.006

Boldsen, J. K., Engedal, T. S., Pedraza, S., Cho, T.-H., Thomalla, G., Nighoghossian, N., Baron, J.-C., Fiehler, J., Østergaard, L., & Mouridsen, K. (2018). Better Diffusion Segmentation in Acute Ischemic Stroke Through Automatic Tree Learning Anomaly Segmentation. Frontiers in Neuroinformatics, 12, 21. https://doi.org/10.3389/fninf.2018.00021

Bonacchi, R., Filippi, M., & Rocca, M. A. (2022). Role of artificial intelligence in MS clinical practice. NeuroImage. Clinical, 35, 103065. https://doi.org/10.1016/j.nicl.2022.103065

Bonacchi, R., Pagani, E., Meani, A., Cacciaguerra, L., Preziosa, P., De Meo, E., Filippi, M., & Rocca, M. A. (2020). Clinical Relevance of Multiparametric MRI Assessment of Cervical Cord Damage in Multiple Sclerosis. Radiology, 296(3), 605–615. https://doi.org/10.1148/radiol.2020200430

Boutet, A., Madhavan, R., Elias, G. J. B., Joel, S. E., Gramer, R., Ranjan, M., Paramanandam, V., Xu, D., Germann, J., Loh, A., Kalia, S. K., Hodaie, M., Li, B., Prasad, S., Coblentz, A., Munhoz, R. P., Ashe, J., Kucharczyk, W., Fasano, A., & Lozano, A. M. (2021). Predicting optimal deep brain stimulation parameters for Parkinson’s disease using functional MRI and machine learning. Nature Communications, 12(1), 3043. https://doi.org/10.1038/s41467-021-23311-9

Broderick, J., Connolly, S., Feldmann, E., Hanley, D., Kase, C., Krieger, D., Mayberg, M., Morgenstern, L., Ogilvy, C. S., Vespa, P., Zuccarello, M., American Heart Association, American Stroke Association Stroke Council, High Blood Pressure Research Council, & Quality of Care and Outcomes in Research Interdisciplinary Working Group. (2007). Guidelines for the management of spontaneous intracerebral hemorrhage in adults: 2007 update: a guideline from the American Heart Association/American Stroke Association Stroke Council, High Blood Pressure Research Council, and the Quality of Care and Outcomes in Research Interdisciplinary Working Group. Stroke; a Journal of Cerebral Circulation, 38(6), 2001–2023. https://doi.org/10.1161/STROKEAHA.107.183689

Bron, E. E., Smits, M., Niessen, W. J., & Klein, S. (2015). Feature Selection Based on the SVM Weight Vector for Classification of Dementia. IEEE Journal of Biomedical and Health Informatics, 19(5), 1617–1626. https://doi.org/10.1109/ JBHI.2015.2432832

Brosch, T., Tang, L. Y. W., Youngjin Yoo, Li, D. K. B., Traboulsee, A., & Tam, R. (2016). Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation. IEEE Transactions on Medical Imaging, 35(5), 1229–1239. https://doi.org/10.1109/TMI.2016.2528821

Cacciaguerra, L., Meani, A., Mesaros, S., Radaelli, M., Palace, J., Dujmovic-Basuroski, I., Pagani, E., Martinelli, V., Matthews, L., Drulovic, J., Leite, M. I., Comi, G., Filippi, M., & Rocca, M. A. (2019). Brain and cord imaging features in neuromyelitis optica spectrum disorders. Annals of Neurology, 85(3), 371–384. https://doi.org/10.1002/ana.25411

Cavedo, E., Tran, P., Thoprakarn, U., Martini, J.-B., Movschin, A., Delmaire, C., Gariel, F., Heidelberg, D., Pyatigorskaya, N., Ströer, S., Krolak-Salmon, P., Cotton, F., Dos Santos, C. L., & Dormont, D. (2022). Validation of an automatic tool for the rapid measurement of brain atrophy and white matter hyperintensity: QyScore^®. European Radiology, 32(5), 2949–2961. https://doi.org/10.1007/s00330-021-08385-9

Chakraborty, S., Aich, S., & Kim, H.-C. (2020). Detection of Parkinson’s Disease from 3T T1 Weighted MRI Scans Using 3D Convolutional Neural Network. Diagnostics, 10(6), 402. https://doi.org/10.3390/diagnostics10060402

Chalela, J. A., Kidwell, C. S., Nentwich, L. M., Luby, M., Butman, J. A., Demchuk, A. M., Hill, M. D., Patronas, N., Latour, L., & Warach, S. (2007). Magnetic resonance imaging and computed tomography in emergency assessment of patients with suspected acute stroke: a prospective comparison. The Lancet, 369(9558), 293–298. <https://doi.org/10.1016/S0140- 6736(07)60151-2

Chang, P. D., Chow, D. S., Yang, P. H., Filippi, C. G., & Lignelli, A. (2017). Predicting Glioblastoma Recurrence by Early Changes in the Apparent Diffusion Coefficient Value and Signal Intensity on FLAIR Images. AJR. American Journal of Roentgenology, 208(1), 57–65. https://doi.org/10.2214/AJR.16.16234

Chang, P. D., Malone, H. R., Bowden, S. G., Chow, D. S., Gill, B. J. A., Ung, T. H., Samanamud, J., Englander, Z. K., Sonabend, A. M., Sheth, S. A., McKhann, G. M., 2nd, Sisti, M. B., Schwartz, L. H., Lignelli, A., Grinband, J., Bruce, J. N., & Canoll, P. (2017). A Multiparametric Model for Mapping Cellularity in Glioblastoma Using Radiographically Localized Biopsies. AJNR. American Journal of Neuroradiology, 38(5), 890–898. https://doi.org/10.3174/ajnr.A5112

Chang, P., Grinband, J., Weinberg, B. D., Bardis, M., Khy, M., Cadena, G., Su, M.-Y., Cha, S., Filippi, C. G., Bota, D., Baldi, P., Poisson, L. M., Jain, R., & Chow, D. (2018). Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas. AJNR. American Journal of Neuroradiology, 39(7), 1201–1207. https://doi.org/10.3174/ajnr.A5667

Cherubini, A., Morelli, M., Nisticó, R., Salsone, M., Arabia, G., Vasta, R., Augimeri, A., Caligiuri, M. E., & Quattrone, A. (2014). Magnetic resonance support vector machine discriminates between Parkinson disease and progressive supranuclear palsy. Movement Disorders: Official Journal of the Movement Disorder Society, 29(2), 266–269. https://doi.org/10.1002/mds.25737

Chien, H.-W. C., Yang, T.-L., Juang, W.-C., Chen, Y.-Y. A., Li, Y.-C. J., & Chen, C.-Y. (2022). Pilot Report for Intracranial Hemorrhage Detection with Deep Learning Implanted Head Computed Tomography Images at Emergency Department. Journal of Medical Systems, 46(7), 49. https://doi.org/10.1007/ s10916-022-01833-z

Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N. G., Venugopal, V. K., Mahajan, V., Rao, P., & Warier, P. (2018). Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. The Lancet, 392(10162), 2388–2396. https://doi.org/10.1016/S0140- 6736(18)31645-3

Choi, H., Ha, S., Im, H. J., Paek, S. H., & Lee, D. S. (2017). Refining diagnosis of Parkinson’s disease with deep learningbased interpretation of dopamine transporter imaging. NeuroImage. Clinical, 16, 586–594. https://doi.org/10.1016/j. nicl.2017.09.010

Connor, S. E. J., Tan, G., Fernando, R., & Chaudhury, N. (2005). Computed tomography pseudofractures of the mid face and skull base. Clinical Radiology, 60(12), 1268–1279. https://doi. org/10.1016/j.crad.2005.05.016

Crous-Bou, M., Minguillón, C., Gramunt, N., & Molinuevo, J. L. (2017). Alzheimer’s disease prevention: from risk factors to early intervention. Alzheimer’s Research & Therapy, 9(1), 1–9. https://doi.org/10.1186/s13195-017-0297-z

Danelakis, A., Theoharis, T., & Verganelakis, D. A. (2018). Survey of automated multiple sclerosis lesion segmentation techniques on magnetic resonance imaging. Computerized Medical Imaging and Graphics: The Official Journal of the Computerized Medical Imaging Society, 70, 83–100. https://doi.org/10.1016/j.compmedimag.2018.10.002

Davatzikos, C., Resnick, S. M., Wu, X., Parmpi, P., & Clark, C. M. (2008). Individual patient diagnosis of AD and FTD via highdimensional pattern classification of MRI. NeuroImage, 41(4), 1220–1227. https://doi.org/10.1016/j.neuroimage.2008.03.050

Dehkharghani, S., Lansberg, M., Venkatsubramanian, C., Cereda, C., Lima, F., Coelho, H., Rocha, F., Qureshi, A., Haerian, H., Mont’Alverne, F., Copeland, K., & Heit, J. (2021). High-Performance Automated Anterior Circulation CT Angiographic Clot Detection in Acute Stroke: A Multireader Comparison. Radiology, 298(3), 665–670. https://doi.org/10.1148/radiol.2021202734

Deshpande, H., Maurel, P., & Barillot, C. (2015). Classification of multiple sclerosis lesions using adaptive dictionary learning. Computerized Medical Imaging and Graphics: The Official Journal of the Computerized Medical Imaging Society, 46 Pt 1 , 2–10. https://doi.org/10.1016/j.compmedimag.2015.05.003

Dolz, J., Desrosiers, C., & Ben Ayed, I. (2018). 3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study. NeuroImage, 170 , 456–470. https://doi.org/10.1016/j.neuroimage.2017.04.039

Duchesne, S., Rolland, Y., & Vérin, M. (2009). Automated computer differential classification in Parkinsonian Syndromes via pattern analysis on MRI. Academic Radiology, 16(1), 61–70. https://doi.org/10.1016/j.acra.2008.05.024

Duvekot, M. H. C., van Es, A. C. G. M., Venema, E., Wolff, L., Rozeman, A. D., Moudrous, W., Vermeij, F. H., Lingsma, H. F., Bakker, J., Plaisier, A. S., Hensen, J.-H. J., Lycklama à Nijeholt, G. J., Jan van Doormaal, P., Dippel, D. W. J., Kerkhoff, H., Roozenbeek, B., & van der Lugt, A. (2021). Accuracy of CTA evaluations in daily clinical practice for large and medium vessel occlusion detection in suspected stroke patients. European Stroke Journal, 23969873211058576. https:// doi.org/10.1177/23969873211058576

Ebinger, M., Galinovic, I., Rozanski, M., Brunecker, P., Endres, M., & Fiebach, J. B. (2010). Fluid-attenuated inversion recovery evolution within 12 hours from stroke onset: a reliable tissue clock? Stroke; a Journal of Cerebral Circulation, 41(2), 250–255. https://doi.org/10.1161/STROKEAHA.109.568410

Eichinger, P., Zimmer, C., & Wiestler, B. (2020). AI in Radiology: Where are we today in Multiple Sclerosis Imaging? RoFo: Fortschritte Auf Dem Gebiete Der Rontgenstrahlen Und Der Nuklearmedizin, 192(9), 847–853. https://doi.org/10.1055/a-1167-8402

Eitel, F., Soehler, E., Bellmann-Strobl, J., Brandt, A. U., Ruprecht, K., Giess, R. M., Kuchling, J., Asseyer, S., Weygandt, M., Haynes, J.-D., Scheel, M., Paul, F., & Ritter, K. (2019). Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layerwise relevance propagation. NeuroImage. Clinical, 24, 102003. https://doi.org/10.1016/j.nicl.2019.102003

EkŞİ, Z., ÇakiroĞlu, M., Öz, C., AralaŞmak, A., Karadelİ, H. H., & Özcan, M. E. (2020). Differentiation of relapsing-remitting and secondary progressive multiple sclerosis: a magnetic resonance spectroscopy study based on machine learning. Arquivos de Neuro-Psiquiatria, 78(12), 789–796. https://doi. org/10.1590/0004-282X20200094

Ekşi, Z., Özcan, M. E., Çakıroğlu, M., Öz, C., & Aralaşmak, A. (2021). Differentiation of multiple sclerosis lesions and lowgrade brain tumors on MRS data: machine learning approaches. Neurological Sciences: Official Journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology, 42(8), 3389–3395. https://doi.org/10.1007/s10072-020-04950-0

Eldaya, R. W., Kansagra, A. P., Zei, M., Mason, E., Holder, D., Heitsch, L., Vo, K. D., & Goyal, M. S. (2022). Performance of Automated RAPID Intracranial Hemorrhage Detection in Real-World Practice: A Single-Institution Experience. Journal of Computer Assisted Tomography, 46(5), 770–774. https://doi.org/10.1097/RCT.0000000000001335

Emon, M. M., Ornob, T. R., & Rahman, M. (2022). Classifications of Skull Fractures using CT Scan Images via CNN with Lazy Learning Approach. In arXiv [eess.IV]. arXiv. http://arxiv.org/abs/2203.10786

Erly, W. K., Berger, W. G., Krupinski, E., Seeger, J. F., & Guisto, J. A. (2002). Radiology resident evaluation of head CT scan orders in the emergency department. AJNR. American Journal of Neuroradiology, 23 (1), 103–107. https://www.ncbi.nlm.nih.gov/ pubmed/11827881

Eshaghi, A., Wottschel, V., Cortese, R., Calabrese, M., Sahraian, M. A., Thompson, A. J., Alexander, D. C., & Ciccarelli, O. (2016). Gray matter MRI differentiates neuromyelitis optica from multiple sclerosis using random forest. Neurology, 87(23), 2463–2470. https://doi.org/10.1212/ WNL.0000000000003395

Feigin, V. L., Vos, T., Nichols, E., Owolabi, M. O., Carroll, W. M., Dichgans, M., Deuschl, G., Parmar, P., Brainin, M., & Murray, C. (2020). The global burden of neurological disorders: translating evidence into policy. Lancet Neurology, 19(3), 255–265. https://doi.org/10.1016/S1474-4422(19)30411-9

Ferreti, L. A., Leitao, C. A., Teixeira, B. C. de A., Lopes Neto, F. D. N., ZÉtola, V. F., & Lange, M. C. (2020). The use of e-ASPECTS in acute stroke care: validation of method performance compared to the performance of specialists. Arquivos de Neuro- Psiquiatria, 78(12), 757–761. https://doi.org/10.1590/0004- 282X20200072

Filippi, M., Preziosa, P., Copetti, M., Riccitelli, G., Horsfield, M. A., Martinelli, V., Comi, G., & Rocca, M. A. (2013). Gray matter damage predicts the accumulation of disability 13 years later in MS. Neurology, 81(20), 1759–1767. https://doi.org/10.1212/01.wnl.0000435551.90824.d0

Flacke, S., Urbach, H., Keller, E., Träber, F., Hartmann, A., Textor, J., Gieseke, J., Block, W., Folkers, P. J., & Schild, H. H. (2000). Middle cerebral artery (MCA) susceptibility sign at susceptibility-based perfusion MR imaging: clinical importance and comparison with hyperdense MCA sign at CT. Radiology, 215(2), 476–482. https://doi.org/10.1148/ radiology.215.2.r00ma09476

Focke, N. K., Helms, G., Scheewe, S., Pantel, P. M., Bachmann, C. G., Dechent, P., Ebentheuer, J., Mohr, A., Paulus, W., & Trenkwalder, C. (2011). Individual voxel-based subtype prediction can differentiate progressive supranuclear palsy from idiopathic Parkinson syndrome and healthy controls. Human Brain Mapping, 32(11), 1905–1915. https://doi.org/10.1002/hbm.21161

Fugate, J. E., & Rabinstein, A. A. (2015). Absolute and Relative Contraindications to IV rt-PA for Acute Ischemic Stroke. The Neurohospitalist, 5(3), 110–121. https://doi. org/10.1177/1941874415578532

Gács, G., Fox, A. J., Barnett, H. J., & Vinuela, F. (1983). CT visualization of intracranial arterial thromboembolism. Stroke; a Journal of Cerebral Circulation, 14(5), 756–762. https://doi. org/10.1161/01.str.14.5.756

GBD 2016 Parkinson’s Disease Collaborators. (2018). Global, regional, and national burden of Parkinson’s disease, 1990- 2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurology, 17(11), 939–953. https://doi. org/10.1016/S1474-4422(18)30295-3

Gibson, E., Georgescu, B., Ceccaldi, P., Trigan, P.-H., Yoo, Y., Das, J., Re, T. J., Rs, V., Balachandran, A., Eibenberger, E., Chekkoury, A., Brehm, B., Bodanapally, U. K., Nicolaou, S., Sanelli, P. C., Schroeppel, T. J., Flohr, T., Comaniciu, D., & Lui, Y. W. (2022). Artificial Intelligence with Statistical Confidence Scores for Detection of Acute or Subacute Hemorrhage on Noncontrast CT Head Scans. Radiology. Artificial Intelligence, 4(3), e210115. https://doi.org/10.1148/ryai.210115

Ginat, D. (2021). Implementation of Machine Learning Software on the Radiology Worklist Decreases Scan View Delay for the Detection of Intracranial Hemorrhage on CT. Brain Sciences, 11(7). https://doi.org/10.3390/brainsci11070832

Goebel, J., Stenzel, E., Guberina, N., Wanke, I., Koehrmann, M., Kleinschnitz, C., Umutlu, L., Forsting, M., Moenninghoff, C., & Radbruch, A. (2018). Automated ASPECT rating: comparison between the Frontier ASPECT Score software and the Brainomix software. Neuroradiology, 60(12), 1267–1272. https://doi.org/10.1007/s00234-018-2098-x

Goldberg-Zimring, D., Achiron, A., Miron, S., Faibel, M., & Azhari, H. (1998). Automated detection and characterization of multiple sclerosis lesions in brain MR images. Magnetic Resonance Imaging, 16(3), 311–318. https://doi.org/10.1016/ s0730-725x(97)00300-7

Guberina, N., Dietrich, U., Radbruch, A., Goebel, J., Deuschl, C., Ringelstein, A., Köhrmann, M., Kleinschnitz, C., Forsting, M., & Mönninghoff, C. (2018). Detection of early infarction signs with machine learning-based diagnosis by means of the Alberta Stroke Program Early CT score (ASPECTS) in the clinical routine. Neuroradiology, 60(9), 889–901. https://doi.org/10.1007/ s00234-018-2066-5

Hakim, A., Christensen, S., Winzeck, S., Lansberg, M. G., Parsons, M. W., Lucas, C., Robben, D., Wiest, R., Reyes, M., & Zaharchuk, G. (2021). Predicting Infarct Core From Computed Tomography Perfusion in Acute Ischemia With Machine Learning: Lessons From the ISLES Challenge. Stroke; a Journal of Cerebral Circulation, 52(7), 2328–2337. https://doi.org/10.1161/STROKEAHA.120.030696

Haller, S., Badoud, S., Nguyen, D., Barnaure, I., Montandon, M.-L., Lovblad, K.-O., & Burkhard, P. R. (2013). Differentiation between Parkinson disease and other forms of Parkinsonism using support vector machine analysis of susceptibilityweighted imaging (SWI): initial results. European Radiology, 23(1), 12–19. https://doi.org/10.1007/s00330-012-2579-y

Haller, S., Badoud, S., Nguyen, D., Garibotto, V., Lovblad, K. O., & Burkhard, P. R. (2012). Individual detection of patients with Parkinson disease using support vector machine analysis of diffusion tensor imaging data: initial results. AJNR. American Journal of Neuroradiology, 33(11), 2123–2128. https://doi.org/10.3174/ajnr.A3126

Han, L., & Kamdar, M. R. (2018). MRI to MGMT: predicting methylation status in glioblastoma patients using convolutional recurrent neural networks. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 23, 331–342. https://www.ncbi.nlm.nih.gov/pubmed/29218894

Han, Y., Yang, Y., Shi, Z.-S., Zhang, A.-D., Yan, L.-F., Hu, Y.-C., Feng, L.-L., Ma, J., Wang, W., & Cui, G.-B. (2021). Distinguishing brain inflammation from grade II glioma in population without contrast enhancement: a radiomics analysis based on conventional MRI. European Journal of Radiology, 134, 109467. https://doi.org/10.1016/j.ejrad.2020.109467

Hassan, A. E., Ringheanu, V. M., Rabah, R. R., Preston, L., Tekle, W. G., & Qureshi, A. I. (2020). Early experience utilizing artificial intelligence shows significant reduction in transfer times and length of stay in a hub and spoke model. Interventional Neuroradiology: Journal of Peritherapeutic Neuroradiology, Surgical Procedures and Related Neurosciences, 26(5), 615–622. https://doi.org/10.1177/1591019920953055

Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P.-M., & Larochelle, H. (2017). Brain tumor segmentation with Deep Neural Networks. Medical Image Analysis, 35, 18–31. https://doi.org/10.1016/j.media.2016.05.004

Heimer, J., Thali, M. J., & Ebert, L. (2018). Classification based on the presence of skull fractures on curved maximum intensity skull projections by means of deep learning. Journal of Forensic Radiology and Imaging, 14, 16–20. https://doi.org/10.1016/j. jofri.2018.08.001

Heit, J. J., Coelho, H., Lima, F. O., Granja, M., Aghaebrahim, A., Hanel, R., Kwok, K., Haerian, H., Cereda, C. W., Venkatasubramanian, C., Dehkharghani, S., Carbonera, L. A., Wiener, J., Copeland, K., & Mont’Alverne, F. (2021). Automated Cerebral Hemorrhage Detection Using RAPID. AJNR. American Journal of Neuroradiology, 42(2), 273–278. https://doi.org/10.3174/ajnr.A6926

Herweh, C., Ringleb, P. A., Rauch, G., Gerry, S., Behrens, L., Möhlenbruch, M., Gottorf, R., Richter, D., Schieber, S., & Nagel, S. (2016). Performance of e-ASPECTS software in comparison to that of stroke physicians on assessing CT scans of acute ischemic stroke patients. International Journal of Stroke: Official Journal of the International Stroke Society, 11(4), 438–445. https://doi.org/10.1177/1747493016632244

Hirschauer, T. J., Adeli, H., & Buford, J. A. (2015). Computer- Aided Diagnosis of Parkinson’s Disease Using Enhanced Probabilistic Neural Network. Journal of Medical Systems, 39(11), 179. https://doi.org/10.1007/s10916-015-0353-9

Hitziger, S., Ling, W. X., Fritz, T., D’Albis, T., Lemke, A., & Grilo, J. (2022). Triplanar U-Net with lesion-wise voting for the segmentation of new lesions on longitudinal MRI studies. Frontiers in Neuroscience, 16, 964250. https://doi.org/10.3389/ fnins.2022.964250

Hoelter, P., Muehlen, I., Goelitz, P., Beuscher, V., Schwab, S., & Doerfler, A. (2020). Automated ASPECT scoring in acute ischemic stroke: comparison of three software tools. Neuroradiology, 62(10), 1231–1238. https://doi.org/10.1007/ s00234-020-02439-3

Hu, L. S., Ning, S., Eschbacher, J. M., Baxter, L. C., Gaw, N., Ranjbar, S., Plasencia, J., Dueck, A. C., Peng, S., Smith, K. A., Nakaji, P., Karis, J. P., Quarles, C. C., Wu, T., Loftus, J. C., Jenkins, R. B., Sicotte, H., Kollmeyer, T. M., O’Neill, B. P., … Mitchell, J. R. (2017). Radiogenomics to characterize regional genetic heterogeneity in glioblastoma. Neuro-Oncology, 19(1), 128–137. https://doi.org/10.1093/neuonc/now135

Huppertz, H.-J., Möller, L., Südmeyer, M., Hilker, R., Hattingen, E., Egger, K., Amtage, F., Respondek, G., Stamelou, M., Schnitzler, A., Pinkhardt, E. H., Oertel, W. H., Knake, S., Kassubek, J., & Höglinger, G. U. (2016). Differentiation of neurodegenerative parkinsonian syndromes by volumetric magnetic resonance imaging analysis and support vector machine classification. Movement Disorders: Official Journal of the Movement Disorder Society, 31(10), 1506– 1517. https://doi.org/10.1002/mds.26715

Hwang, D. Y., Silva, G. S., Furie, K. L., & Greer, D. M. (2012). Comparative sensitivity of computed tomography vs. magnetic resonance imaging for detecting acute posterior fossa infarct. The Journal of Emergency Medicine, 42(5), 559–565. https://doi. org/10.1016/j.jemermed.2011.05.101

Ion-Mărgineanu, A., Kocevar, G., Stamile, C., Sima, D. M., Durand-Dubief, F., Van Huffel, S., & Sappey-Marinier, D. (2017). Machine Learning Approach for Classifying Multiple Sclerosis Courses by Combining Clinical Data with Lesion Loads and Magnetic Resonance Metabolic Features. Frontiers in Neuroscience, 11, 398. https://doi.org/10.3389/fnins.2017.00398

Jain, A., Malhotra, A., & Payabvash, S. (2021). Imaging of Spontaneous Intracerebral Hemorrhage. Neuroimaging Clinics of North America, 31(2), 193–203. https://doi.org/10.1016/j. nic.2021.02.003

Jain, S., Ribbens, A., Sima, D. M., Cambron, M., De Keyser, J., Wang, C., Barnett, M. H., Van Huffel, S., Maes, F., & Smeets, D. (2016). Two Time Point MS Lesion Segmentation in Brain MRI: An Expectation-Maximization Framework. Frontiers in Neuroscience, 10, 576. https://doi.org/10.3389/fnins.2016.00576

Jang, B.-S., Jeon, S. H., Kim, I. H., & Kim, I. A. (2018). Prediction of Pseudoprogression versus Progression using Machine Learning Algorithm in Glioblastoma. Scientific Reports, 8(1), 12516. https://doi.org/10.1038/s41598-018-31007-2

Jang, B.-S., Park, A. J., Jeon, S. H., Kim, I. H., Lim, D. H., Park, S.-H., Lee, J. H., Chang, J. H., Cho, K. H., Kim, J. H., Sunwoo, L., Choi, S. H., & Kim, I. A. (2020). Machine Learning Model to Predict Pseudoprogression Versus Progression in Glioblastoma Using MRI: A Multi-Institutional Study (KROG 18-07). Cancers, 12(9). https://doi.org/10.3390/cancers12092706

Jayachandran Preetha, C., Meredig, H., Brugnara, G., Mahmutoglu, M. A., Foltyn, M., Isensee, F., Kessler, T., Pflüger, I., Schell, M., Neuberger, U., Petersen, J., Wick, A., Heiland, S., Debus, J., Platten, M., Idbaih, A., Brandes, A. A., Winkler, F., van den Bent, M. J., … Vollmuth, P. (2021). Deeplearning- based synthesis of post-contrast T1-weighted MRI for tumour response assessment in neuro-oncology: a multicentre, retrospective cohort study. The Lancet. Digital Health, 3(12), e784–e794. https://doi.org/10.1016/S2589-7500(21)00205-3

Jiang, L., Wang, S., Ai, Z., Shen, T., Zhang, H., Duan, S., Chen, Y.-C., Yin, X., & Sun, J. (2022). Development and external validation of a stability machine learning model to identify wake-up stroke onset time from MRI. European Radiology, 32(6), 3661–3669. https://doi.org/10.1007/s00330-021-08493-6

Kanber, B., Nachev, P., Barkhof, F., Calvi, A., Cardoso, J., Cortese, R., Prados, F., Sudre, C. H., Tur, C., Ourselin, S., & Ciccarelli, O. (2019). High-dimensional detection of imaging response to treatment in multiple sclerosis. NPJ Digital Medicine, 2, 49. https://doi.org/10.1038/s41746-019-0127-8

Karimian, A., & Jafari, S. (2015). A New Method to Segment the Multiple Sclerosis Lesions on Brain Magnetic Resonance Images. Journal of Medical Signals and Sensors, 5(4), 238–244. https:// www.ncbi.nlm.nih.gov/pubmed/26955567

Kickingereder, P., Bonekamp, D., Nowosielski, M., Kratz, A., Sill, M., Burth, S., Wick, A., Eidel, O., Schlemmer, H.-P., Radbruch, A., Debus, J., Herold-Mende, C., Unterberg, A., Jones, D., Pfister, S., Wick, W., von Deimling, A., Bendszus, M., & Capper, D. (2016). Radiogenomics of Glioblastoma: Machine Learning-based Classification of Molecular Characteristics by Using Multiparametric and Multiregional MR Imaging Features. Radiology, 281(3), 907–918. https://doi. org/10.1148/radiol.2016161382

Kickingereder, P., Isensee, F., Tursunova, I., Petersen, J., Neuberger, U., Bonekamp, D., Brugnara, G., Schell, M., Kessler, T., Foltyn, M., Harting, I., Sahm, F., Prager, M., Nowosielski, M., Wick, A., Nolden, M., Radbruch, A., Debus, J., Schlemmer, H.-P., … Maier-Hein, K. H. (2019). Automated quantitative tumour response assessment of MRI in neurooncology with artificial neural networks: a multicentre, retrospective study. The Lancet Oncology, 20(5), 728–740. https://doi.org/10.1016/S1470-2045(19)30098-1

Kim, B. J., Kim, Y.-H., Kim, Y.-J., Ahn, S. H., Lee, D. H., Kwon, S. U., Kim, S. J., Kim, J. S., & Kang, D.-W. (2014). Color-coded fluid-attenuated inversion recovery images improve inter-rater reliability of fluid-attenuated inversion recovery signal changes within acute diffusion-weighted image lesions. Stroke; a Journal of Cerebral Circulation, 45(9), 2801–2804. https://doi.org/10.1161/STROKEAHA.114.006515

Kim, J. Y., Park, J. E., Jo, Y., Shim, W. H., Nam, S. J., Kim, J. H., Yoo, R.-E., Choi, S. H., & Kim, H. S. (2019). Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improves diagnostic performance for pseudoprogression in glioblastoma patients. Neuro-Oncology, 21(3), 404–414. https://doi.org/10.1093/neuonc/noy133

Kim, M., Kim, H. S., Kim, H. J., Park, J. E., Park, S. Y., Kim, Y.- H., Kim, S. J., Lee, J., & Lebel, M. R. (2021). Thin-Slice Pituitary MRI with Deep Learning-based Reconstruction: Diagnostic Performance in a Postoperative Setting. Radiology, 298(1), 114–122. https://doi.org/10.1148/radiol.2020200723

Kniep, H. C., Madesta, F., Schneider, T., Hanning, U., Schönfeld, M. H., Schön, G., Fiehler, J., Gauer, T., Werner, R., & Gellissen, S. (2019). Radiomics of Brain MRI: Utility in Prediction of Metastatic Tumor Type. Radiology, 290(2), 479–487. https://doi.org/10.1148/radiol.2018180946

Kniep, H. C., Sporns, P. B., Broocks, G., Kemmling, A., Nawabi, J., Rusche, T., Fiehler, J., & Hanning, U. (2020). Posterior circulation stroke: machine learning-based detection of early ischemic changes in acute non-contrast CT scans. Journal of Neurology, 267(9), 2632–2641. https://doi. org/10.1007/s00415-020-09859-4

Knopman, D. S., Amieva, H., Petersen, R. C., Chételat, G., Holtzman, D. M., Hyman, B. T., Nixon, R. A., & Jones, D. T. (2021). Alzheimer disease. Nature Reviews. Disease Primers, 7(1), 33. https://doi.org/10.1038/s41572-021-00269-y

Kocevar, G., Stamile, C., Hannoun, S., Cotton, F., Vukusic, S., Durand-Dubief, F., & Sappey-Marinier, D. (2016). Graph Theory-Based Brain Connectivity for Automatic Classification of Multiple Sclerosis Clinical Courses. Frontiers in Neuroscience, 10, 478. https://doi.org/10.3389/fnins.2016.00478

Kouli, O., Hassane, A., Badran, D., Kouli, T., Hossain-Ibrahim, K., & Steele, J. D. (2022). Automated brain tumor identification using magnetic resonance imaging: A systematic review and meta-analysis. Neuro-Oncology Advances, 4(1), vdac081. https:// doi.org/10.1093/noajnl/vdac081

Kuang, H., Najm, M., Chakraborty, D., Maraj, N., Sohn, S. I., Goyal, M., Hill, M. D., Demchuk, A. M., Menon, B. K., & Qiu, W. (2019). Automated ASPECTS on Noncontrast CT Scans in Patients with Acute Ischemic Stroke Using Machine Learning. AJNR. American Journal of Neuroradiology, 40(1), 33–38. https://doi.org/10.3174/ajnr.A5889

Kuang, Z., Deng, X., Yu, L., Zhang, H., Lin, X., & Ma, H. (2020). Skull R-CNN: A CNN-based network for the skull fracture detection. In T. Arbel, I. Ben Ayed, M. de Bruijne, M. Descoteaux, H. Lombaert, & C. Pal (Eds.), Proceedings of the Third Conference on Medical Imaging with Deep Learning (Vol. 121, pp. 382–392). PMLR. https://proceedings.mlr.press/v121/kuang20a.html

Kushibar, K., Valverde, S., González-Villà, S., Bernal, J., Cabezas, M., Oliver, A., & Lladó, X. (2018). Automated sub-cortical brain structure segmentation combining spatial and deep convolutional features. Medical Image Analysis, 48, 177–186. https://doi.org/10.1016/j.media.2018.06.006

Lamptey, R. N. L., Chaulagain, B., Trivedi, R., Gothwal, A., Layek, B., & Singh, J. (2022). A Review of the Common Neurodegenerative Disorders: Current Therapeutic Approaches and the Potential Role of Nanotherapeutics. International Journal of Molecular Sciences, 23(3). https://doi.org/10.3390/ ijms23031851

Leao, D. J., Craig, P. G., Godoy, L. F., Leite, C. C., & Policeni, B. (2020). Response Assessment in Neuro-Oncology Criteria for Gliomas: Practical Approach Using Conventional and Advanced Techniques. AJNR. American Journal of Neuroradiology, 41(1), 10–20. https://doi.org/10.3174/ajnr.A6358

Lebedev, A. V., Westman, E., Van Westen, G. J. P., Kramberger, M. G., Lundervold, A., Aarsland, D., Soininen, H., Kłoszewska, I., Mecocci, P., Tsolaki, M., Vellas, B., Lovestone, S., Simmons, A., & Alzheimer’s Disease Neuroimaging Initiative and the AddNeuroMed consortium. (2014). Random Forest ensembles for detection and prediction of Alzheimer’s disease with a good between-cohort robustness. NeuroImage. Clinical, 6, 115–125. https://doi.org/10.1016/j. nicl.2014.08.023

Lee, D. H., Park, J. E., Nam, Y. K., Lee, J., Kim, S., Kim, Y.-H., & Kim, H. S. (2021). Deep learning-based thin-section MRI reconstruction improves tumour detection and delineation in pre- and post-treatment pituitary adenoma. Scientific Reports, 11(1), 21302. https://doi.org/10.1038/s41598-021-00558-2

Lee, G., Nho, K., Kang, B., Sohn, K.-A., Kim, D., & for Alzheimer’s Disease Neuroimaging Initiative. (2019). Predicting Alzheimer’s disease progression using multi-modal deep learning approach. Scientific Reports, 9(1), 1952. https://doi.org/10.1038/s41598-018-37769-z

Lee, H., Lee, E.-J., Ham, S., Lee, H.-B., Lee, J. S., Kwon, S. U., Kim, J. S., Kim, N., & Kang, D.-W. (2020). Machine Learning Approach to Identify Stroke Within 4.5 Hours. Stroke; a Journal of Cerebral Circulation, 51(3), 860–866. https://doi.org/10.1161/ STROKEAHA.119.027611

Lisowska, A., O’Neil, A., Dilys, V., Daykin, M., Beveridge, E., Muir, K., Mclaughlin, S., & Poole, I. (2017). Context-Aware Convolutional Neural Networks for Stroke Sign Detection in Noncontrast CT Scans. Medical Image Understanding and Analysis, 494–505. https://doi.org/10.1007/978-3-319-60964-5_43

Long, D., Wang, J., Xuan, M., Gu, Q., Xu, X., Kong, D., & Zhang, M. (2012). Automatic classification of early Parkinson’s disease with multi-modal MR imaging. PloS One, 7(11), e47714. https://doi.org/10.1371/journal.pone.0047714

Lopatina, A., Ropele, S., Sibgatulin, R., Reichenbach, J. R., & Güllmar, D. (2020). Investigation of Deep-Learning- Driven Identification of Multiple Sclerosis Patients Based on Susceptibility-Weighted Images Using Relevance Analysis. Frontiers in Neuroscience, 14, 609468. https://doi.org/10.3389/ fnins.2020.609468

Lublin, F. D., Reingold, S. C., Cohen, J. A., Cutter, G. R., Sørensen, P. S., Thompson, A. J., Wolinsky, J. S., Balcer, L. J., Banwell, B., Barkhof, F., Bebo, B., Jr, Calabresi, P. A., Clanet, M., Comi, G., Fox, R. J., Freedman, M. S., Goodman, A. D., Inglese, M., Kappos, L., … Polman, C. H. (2014). Defining the clinical course of multiple sclerosis: the 2013 revisions. Neurology, 83(3), 278–286. https://doi.org/10.1212/ WNL.0000000000000560

Lu, D., Popuri, K., Ding, G. W., Balachandar, R., Beg, M. F., & Alzheimer’s Disease Neuroimaging Initiative. (2018). Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer’s Disease using structural MR and FDG-PET images. Scientific Reports, 8(1), 5697. https://doi. org/10.1038/s41598-018-22871-z

Mackey J, Kleindorfer D, Sucharew H, et al. Population-based study of wake-up strokes. Neurology. 2011;76(19):1662-1667. doi:10.1212/WNL.0b013e318219fb30

Maegerlein, C., Fischer, J., Mönch, S., Berndt, M., Wunderlich, S., Seifert, C. L., Lehm, M., Boeckh-Behrens, T., Zimmer, C., & Friedrich, B. (2019). Automated Calculation of the Alberta Stroke Program Early CT Score: Feasibility and Reliability. Radiology, 291(1), 141–148. https://doi.org/10.1148/ radiol.2019181228

Mangeat, G., Ouellette, R., Wabartha, M., De Leener, B., Plattén, M., Danylaité Karrenbauer, V., Warntjes, M., Stikov, N., Mainero, C., Cohen-Adad, J., & Granberg, T. (2020). Machine Learning and Multiparametric Brain MRI to Differentiate Hereditary Diffuse Leukodystrophy with Spheroids from Multiple Sclerosis. Journal of Neuroimaging: Official Journal of the American Society of Neuroimaging, 30(5), 674–682. https://doi.org/10.1111/jon.12725

Marks, M. P., Holmgren, E. B., Fox, A. J., Patel, S., von Kummer, R., & Froehlich, J. (1999). Evaluation of early computed tomographic findings in acute ischemic stroke. Stroke; a Journal of Cerebral Circulation, 30(2), 389–392. https://doi.org/10.1161/01.str.30.2.389

Marquand, A. F., Filippone, M., Ashburner, J., Girolami, M., Mourao-Miranda, J., Barker, G. J., Williams, S. C. R., Leigh, P. N., & Blain, C. R. V. (2013). Automated, high accuracy classification of Parkinsonian disorders: a pattern recognition approach. PloS One, 8(7), e69237. https://doi.org/10.1371/ journal.pone.0069237

Marzullo, A., Kocevar, G., Stamile, C., Durand-Dubief, F., Terracina, G., Calimeri, F., & Sappey-Marinier, D. (2019). Classification of Multiple Sclerosis Clinical Profiles via Graph Convolutional Neural Networks. Frontiers in Neuroscience, 13, 594. https://doi.org/10.3389/fnins.2019.00594

Matthews, P. M., Roncaroli, F., Waldman, A., Sormani, M. P., De Stefano, N., Giovannoni, G., & Reynolds, R. (2016). A practical review of the neuropathology and neuroimaging of multiple sclerosis. Practical Neurology, 16(4), 279–287. https://doi.org/10.1136/practneurol-2016-001381

McGinley, M. P., Goldschmidt, C. H., & Rae-Grant, A. D. (2021). Diagnosis and Treatment of Multiple Sclerosis: A Review.JAMA: The Journal of the American Medical Association, 325(8), 765–779. https://doi.org/10.1001/jama.2020.26858

McLouth, J., Elstrott, S., Chaibi, Y., Quenet, S., Chang, P. D., Chow, D. S., & Soun, J. E. (2021). Validation of a Deep Learning Tool in the Detection of Intracranial Hemorrhage and Large Vessel Occlusion. Frontiers in Neurology, 12, 656112. https://doi.org/10.3389/fneur.2021.656112

Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., Lanczi, L., Gerstner, E., Weber, M.-A., Arbel, T., Avants, B. B., Ayache, N., Buendia, P., Collins, D. L., Cordier, N., … Van Leemput, K. (2015). The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). IEEE Transactions on Medical Imaging, 34(10), 1993–2024. https://doi.org/10.1109/ TMI.2014.2377694

Merkaj, S., Bahar, R. C., Zeevi, T., Lin, M., Ikuta, I., Bousabarah, K., Cassinelli Petersen, G. I., Staib, L., Payabvash, S., Mongan, J. T., Cha, S., & Aboian, M. S. (2022). Machine Learning Tools for Image-Based Glioma Grading and the Quality of Their Reporting: Challenges and Opportunities. Cancers, 14(11). https://doi.org/10.3390/cancers14112623

Moazami, F., Lefevre-Utile, A., Papaloukas, C., & Soumelis, V. (2021). Machine Learning Approaches in Study of Multiple Sclerosis Disease Through Magnetic Resonance Images. Frontiers in Immunology, 12, 700582. https://doi.org/10.3389/ fimmu.2021.700582

Mohd Saad, N., Abdullah, A. R., Mohd Noor, N. S., & Mohd Ali, N. (2019). Automated Segmentation And Classification Technique For Brain Stroke. International Journal of Electrical, Computer, and Systems Engineering, 9(3), 1832–1841. https://doi.org/10.11591/ijece.v9i3.pp1832-1841

Mokin, M., Ansari, S. A., McTaggart, R. A., Bulsara, K. R., Goyal, M., Chen, M., Fraser, J. F., & Society of NeuroInterventional Surgery. (2019). Indications for thrombectomy in acute ischemic stroke from emergent large vessel occlusion (ELVO): report of the SNIS Standards and Guidelines Committee. Journal of Neurointerventional Surgery, 11(3), 215–220. https://doi.org/10.1136/ neurintsurg-2018-014640

Moradi, E., Pepe, A., Gaser, C., Huttunen, H., Tohka, J., & Alzheimer’s Disease Neuroimaging Initiative. (2015). Machine learning framework for early MRI-based Alzheimer’s conversion prediction in MCI subjects. NeuroImage, 104, 398–412. https://doi.org/10.1016/j.neuroimage.2014.10.002

Morey, J. R., Zhang, X., Yaeger, K. A., Fiano, E., Marayati, N. F., Kellner, C. P., De Leacy, R. A., Doshi, A., Tuhrim, S., & Fifi, J. T. (2021). Real-World Experience with Artificial Intelligence- Based Triage in Transferred Large Vessel Occlusion Stroke Patients. Cerebrovascular Diseases, 50(4), 450–455. https://doi.org/10.1159/000515320

Murray, N. M., Unberath, M., Hager, G. D., & Hui, F. K. (2020). Artificial intelligence to diagnose ischemic stroke and identify large vessel occlusions: a systematic review. Journal of Neurointerventional Surgery, 12(2), 156–164. https://doi.org/10.1136/neurintsurg-2019-015135

Nagel, S., Sinha, D., Day, D., Reith, W., Chapot, R., Papanagiotou, P., Warburton, E. A., Guyler, P., Tysoe, S., Fassbender, K., Walter, S., Essig, M., Heidenrich, J., Konstas, A. A., Harrison, M., Papadakis, M., Greveson, E., Joly, O., Gerry, S., … Grunwald, I. Q. (2017). e-ASPECTS software is non-inferior to neuroradiologists in applying the ASPECT score to computed tomography scans of acute ischemic stroke patients. International Journal of Stroke: Official Journal of the International Stroke Society, 12(6), 615–622. https://doi. org/10.1177/1747493016681020

Nazari-Farsani, S., Nyman, M., Karjalainen, T., Bucci, M., Isojärvi, J., & Nummenmaa, L. (2020). Automated segmentation of acute stroke lesions using a data-driven anomaly detection on diffusion weighted MRI. Journal of Neuroscience Methods, 333, 108575. https://doi.org/10.1016/j. jneumeth.2019.108575

Neeb, H., & Schenk, J. (2019). Multivariate prediction of multiple sclerosis using robust quantitative MR-based image metrics. Zeitschrift Fur Medizinische Physik, 29(3), 262–271. https://doi.org/10.1016/j.zemedi.2018.10.004

Ocasio, E., & Duong, T. Q. (2021). Deep learning prediction of mild cognitive impairment conversion to Alzheimer’s disease at 3 years after diagnosis using longitudinal and whole-brain 3D MRI. PeerJ. Computer Science, 7, e560. https://doi.org/10.7717/ peerj-cs.560

Olive-Gadea, M., Crespo, C., Granes, C., Hernandez-Perez, M., Pérez de la Ossa, N., Laredo, C., Urra, X., Carlos Soler, J., Soler, A., Puyalto, P., Cuadras, P., Marti, C., & Ribo, M. (2020). Deep Learning Based Software to Identify Large Vessel Occlusion on Noncontrast Computed Tomography. Stroke; a Journal of Cerebral Circulation, 51(10), 3133–3137. https://doi.org/10.1161/ STROKEAHA.120.030326

Olive-Gadea, M., Martins, N., Boned, S., Carvajal, J., Moreno, M. J., Muchada, M., Molina, C. A., Tomasello, A., Ribo, M., & Rubiera, M. (2019). Baseline ASPECTS and e-ASPECTS Correlation with Infarct Volume and Functional Outcome in Patients Undergoing Mechanical Thrombectomy. Journal of Neuroimaging: Official Journal of the American Society of Neuroimaging, 29(2), 198–202. https://doi.org/10.1111/ jon.12564

Olthof, A. W., van Ooijen, P. M. A., & Rezazade Mehrizi, M. H. (2020). Promises of artificial intelligence in neuroradiology: a systematic technographic review. Neuroradiology, 62(10), 1265–1278. https://doi.org/10.1007/s00234-020-02424-w

Öman, O., Mäkelä, T., Salli, E., Savolainen, S., & Kangasniemi, M. (2019). 3D convolutional neural networks applied to CT angiography in the detection of acute ischemic stroke. European Radiology Experimental, 3(1), 8. https://doi.org/10.1186/s41747-019-0085-6

Ozsahin, I., Sekeroglu, B., Pwavodi, P. C., & Mok, G. S. P. (2020). High-accuracy Automated Diagnosis of Parkinson’s Disease. Current Medical Imaging Reviews, 16(6), 688–694. https://doi.org/10.2174/1573405615666190620113607

Pagan, F. L. (2012). Improving outcomes through early diagnosis of Parkinson’s disease. The American Journal of Managed Care, 18(7 Suppl), S176–S182. https://www.ncbi.nlm. nih.gov/pubmed/23039866

Park, J. E., Kim, H. S., Park, S. Y., Nam, S. J., Chun, S.-M., Jo, Y., & Kim, J. H. (2020). Prediction of Core Signaling Pathway by Using Diffusion- and Perfusion-based MRI Radiomics and Next-generation Sequencing in Isocitrate Dehydrogenase Wildtype Glioblastoma. Radiology, 294(2), 388–397. https://doi. org/10.1148/radiol.2019190913

Peng, B., Wang, S., Zhou, Z., Liu, Y., Tong, B., Zhang, T., & Dai, Y. (2017). A multilevel-ROI-features-based machine learning method for detection of morphometric biomarkers in Parkinson’s disease. Neuroscience Letters, 651, 88–94. https://doi. org/10.1016/j.neulet.2017.04.034

Petersen, R. C. (2016). Mild Cognitive Impairment. Continuum, 22(2 Dementia), 404–418. https://doi.org/10.1212/CON.0000000000000313

Piccardo, A., Cappuccio, R., Bottoni, G., Cecchin, D., Mazzella, L., Cirone, A., Righi, S., Ugolini, M., Bianchi, P., Bertolaccini, P., Lorenzini, E., Massollo, M., Castaldi, A., Fiz, F., Strada, L., Cistaro, A., & Del Sette, M. (2021). The role of the deep convolutional neural network as an aid to interpreting brain [18F]DOPA PET/CT in the diagnosis of Parkinson’s disease. European Radiology, 31(9), 7003–7011. https://doi.org/10.1007/ s00330-021-07779-z

Pirson, F. A. V., Boodt, N., Brouwer, J., Bruggeman, A. A. E., den Hartog, S. J., Goldhoorn, R.-J. B., Langezaal, L. C. M., Staals, J., van Zwam, W. H., van der Leij, C., Brans, R. J. B., Majoie, C. B. L. M., Coutinho, J. M., Emmer, B. J., Dippel, D. W. J., van der Lugt, A., Vos, J.-A., van Oostenbrugge, R. J., Schonewille, W. J., & MR CLEAN Registry Investigators†. (2022). Endovascular Treatment for Posterior Circulation Stroke in Routine Clinical Practice: Results of the Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands Registry. Stroke; a Journal of Cerebral Circulation, 53(3), 758–768. https://doi.org/10.1161/ STROKEAHA.121.034786

Pläschke, R. N., Cieslik, E. C., Müller, V. I., Hoffstaedter, F., Plachti, A., Varikuti, D. P., Goosses, M., Latz, A., Caspers, S., Jockwitz, C., Moebus, S., Gruber, O., Eickhoff, C. R., Reetz, K., Heller, J., Südmeyer, M., Mathys, C., Caspers, J., Grefkes, C., … Eickhoff, S. B. (2017). On the integrity of functional brain networks in schizophrenia, Parkinson’s disease, and advanced age: Evidence from connectivity-based single-subject classification. Human Brain Mapping, 38(12), 5845–5858. https://doi.org/10.1002/hbm.23763

Polson, J. S., Zhang, H., Nael, K., Salamon, N., Yoo, B. Y., El-Saden, S., Starkman, S., Kim, N., Kang, D.-W., Speier, W. F., 4th, & Arnold, C. W. (2022). Identifying acute ischemic stroke patients within the thrombolytic treatment window using deep learning. Journal of Neuroimaging: Official Journal of the American Society of Neuroimaging. https://doi.org/10.1111/jon.13043

Potreck, A., Weyland, C. S., Seker, F., Neuberger, U., Herweh, C., Hoffmann, A., Nagel, S., Bendszus, M., & Mutke, M. A. (2022). Accuracy and prognostic role of NCCT-ASPECTS depend on time from acute stroke symptom-onset for both human and machine-learning based evaluation. Clinical Neuroradiology, 32(1), 133–140. https://doi.org/10.1007/s00062-021-01110-5

Powers, W. J., Rabinstein, A. A., Ackerson, T., Adeoye, O. M., Bambakidis, N. C., Becker, K., Biller, J., Brown, M., Demaerschalk, B. M., Hoh, B., Jauch, E. C., Kidwell, C. S., Leslie-Mazwi, T. M., Ovbiagele, B., Scott, P. A., Sheth, K. N., Southerland, A. M., Summers, D. V., & Tirschwell, D. L. (2018). 2018 Guidelines for the Early Management of Patients With Acute Ischemic Stroke: A Guideline for Healthcare Professionals From the American Heart Association/American Stroke Association. Stroke; a Journal of Cerebral Circulation, 49(3), e46–e110. https://doi.org/10.1161/STR.0000000000000158

Qiu, W., Kuang, H., Teleg, E., Ospel, J. M., Sohn, S. I., Almekhlafi, M., Goyal, M., Hill, M. D., Demchuk, A. M., & Menon, B. K. (2020). Machine Learning for Detecting Early Infarction in Acute Stroke with Non-Contrast-enhanced CT. Radiology, 294(3), 638–644. https://doi.org/10.1148/ radiol.2020191193

Qubiotech. (2021, November 9). Qubiotech. https://qubiotech.com/en/resources/

Qureshi, A. I., Mendelow, A. D., & Hanley, D. F. (2009). Intracerebral haemorrhage. The Lancet, 373(9675), 1632–1644. https://doi.org/10.1016/S0140-6736(09)60371-8

Raimbault, A., Cazals, X., Lauvin, M.-A., Destrieux, C., Chapet, S., & Cottier, J.-P. (2014). Radionecrosis of malignant glioma and cerebral metastasis: a diagnostic challenge in MRI. Diagnostic and Interventional Imaging, 95(10), 985–1000. https:// doi.org/10.1016/j.diii.2014.06.013

Rao, B., Zohrabian, V., Cedeno, P., Saha, A., Pahade, J., & Davis, M. A. (2021). Utility of Artificial Intelligence Tool as a Prospective Radiology Peer Reviewer - Detection of Unreported Intracranial Hemorrhage. Academic Radiology, 28(1), 85–93. https://doi.org/10.1016/j.acra.2020.01.035

Rava, R. A., Peterson, B. A., Seymour, S. E., Snyder, K. V., Mokin, M., Waqas, M., Hoi, Y., Davies, J. M., Levy, E. I., Siddiqui, A. H., & Ionita, C. N. (2021). Validation of an artificial intelligence-driven large vessel occlusion detection algorithm for acute ischemic stroke patients. The Neuroradiology Journal, 34(5), 408–417. https://doi.org/10.1177/1971400921998952

Rava, R. A., Seymour, S. E., LaQue, M. E., Peterson, B. A., Snyder, K. V., Mokin, M., Waqas, M., Hoi, Y., Davies, J. M., Levy, E. I., Siddiqui, A. H., & Ionita, C. N. (2021). Assessment of an Artificial Intelligence Algorithm for Detection of Intracranial Hemorrhage. World Neurosurgery, 150, e209–e217. https://doi. org/10.1016/j.wneu.2021.02.134

Rezazade Mehrizi, M. H., van Ooijen, P., & Homan, M. (2021). Applications of artificial intelligence (AI) in diagnostic radiology: a technography study. European Radiology, 31(4), 1805–1811. https://doi.org/10.1007/s00330-020-07230-9

Rizzo, G., Copetti, M., Arcuti, S., Martino, D., Fontana, A., & Logroscino, G. (2016). Accuracy of clinical diagnosis of Parkinson disease: A systematic review and metaanalysis. Neurology, 86(6), 566–576. https://doi.org/10.1212/ WNL.0000000000002350

Roca, P., Attye, A., Colas, L., Tucholka, A., Rubini, P., Cackowski, S., Ding, J., Budzik, J.-F., Renard, F., Doyle, S., Barbier, E. L., Bousaid, I., Casey, R., Vukusic, S., Lassau, N., Verclytte, S., Cotton, F., OFSEP Investigators, Steering Committee, … Imaging group. (2020). Artificial intelligence to predict clinical disability in patients with multiple sclerosis using FLAIR MRI. Diagnostic and Interventional Imaging, 101(12), 795–802. https://doi.org/10.1016/j.diii.2020.05.009

Rocca, M. A., Anzalone, N., Storelli, L., Del Poggio, A., Cacciaguerra, L., Manfredi, A. A., Meani, A., & Filippi, M. (2021). Deep Learning on Conventional Magnetic Resonance Imaging Improves the Diagnosis of Multiple Sclerosis Mimics. Investigative Radiology, 56(4), 252–260. https://doi.org/10.1097/ RLI.0000000000000735

Rotstein, D., & Montalban, X. (2019). Reaching an evidencebased prognosis for personalized treatment of multiple sclerosis. Nature Reviews. Neurology, 15(5), 287–300. https://doi.org/10.1038/s41582-019-0170-8

Roy, S., Butman, J. A., Reich, D. S., Calabresi, P. A., & Pham, D. L. (2018). Multiple Sclerosis Lesion Segmentation from Brain MRI via Fully Convolutional Neural Networks. In arXiv [cs.CV]. arXiv. http://arxiv.org/abs/1803.09172

Saccà, V., Sarica, A., Novellino, F., Barone, S., Tallarico, T., Filippelli, E., Granata, A., Chiriaco, C., Bruno Bossio, R., Valentino, P., & Quattrone, A. (2019). Evaluation of machine learning algorithms performance for the prediction of early multiple sclerosis from resting-state FMRI connectivity data. Brain Imaging and Behavior, 13(4), 1103–1114. https://doi.org/10.1007/s11682-018-9926-9

Salvatore, C., Cerasa, A., Battista, P., Gilardi, M. C., Quattrone, A., Castiglioni, I., & Alzheimer’s Disease Neuroimaging Initiative. (2015). Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer’s disease: a machine learning approach. Frontiers in Neuroscience, 9, 307. https://doi.org/10.3389/fnins.2015.00307

Salvatore, C., Cerasa, A., Castiglioni, I., Gallivanone, F., Augimeri, A., Lopez, M., Arabia, G., Morelli, M., Gilardi, M. C., & Quattrone, A. (2014). Machine learning on brain MRI data for differential diagnosis of Parkinson’s disease and Progressive Supranuclear Palsy. Journal of Neuroscience Methods, 222, 230–237. https://doi.org/10.1016/j.jneumeth.2013.11.016

Samarasekera, S., Udupa, J. K., Miki, Y., Wei, L., & Grossman, R. I. (1997). A new computer-assisted method for the quantification of enhancing lesions in multiple sclerosis. Journal of Computer Assisted Tomography, 21(1), 145–151. https://doi.org/10.1097/00004728-199701000-00028

Schmidt, P., Gaser, C., Arsic, M., Buck, D., Förschler, A., Berthele, A., Hoshi, M., Ilg, R., Schmid, V. J., Zimmer, C., Hemmer, B., & Mühlau, M. (2012). An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis. NeuroImage, 59(4), 3774–3783. https://doi.org/10.1016/j.neuroimage.2011.11.032

Schmitt, N., Mokli, Y., Weyland, C. S., Gerry, S., Herweh, C., Ringleb, P. A., & Nagel, S. (2022). Automated detection and segmentation of intracranial hemorrhage suspect hyperdensities in non-contrast-enhanced CT scans of acute stroke patients. European Radiology, 32(4), 2246–2254. https://doi.org/10.1007/s00330-021-08352-4

Schröder, J., & Thomalla, G. (2016). A Critical Review of Alberta Stroke Program Early CT Score for Evaluation of Acute Stroke Imaging. Frontiers in Neurology, 7, 245. https://doi.org/10.3389/ fneur.2016.00245

Schweitzer, A. D., Niogi, S. N., Whitlow, C. T., & Tsiouris, A. J. (2019). Traumatic Brain Injury: Imaging Patterns and Complications. Radiographics: A Review Publication of the Radiological Society of North America, Inc, 39(6), 1571–1595. https://doi.org/10.1148/rg.2019190076

Seker, F., Pfaff, J., Nagel, S., Vollherbst, D., Gerry, S., Möhlenbruch, M. A., Bendszus, M., & Herweh, C. (2019). CT Reconstruction Levels Affect Automated and Reader- Based ASPECTS Ratings in Acute Ischemic Stroke. Journal of Neuroimaging: Official Journal of the American Society of Neuroimaging, 29(1), 62–64. https://doi.org/10.1111/jon.12562

Siddique, M. M. R., Yang, D., He, Y., Xu, D., & Myronenko, A. (2022). Automated ischemic stroke lesion segmentation from 3D MRI. In arXiv [eess.IV]. arXiv. http://arxiv.org/abs/2209.09546

Singh, G., Manjila, S., Sakla, N., True, A., Wardeh, A. H., Beig, N., Vaysberg, A., Matthews, J., Prasanna, P., & Spektor, V. (2021). Radiomics and radiogenomics in gliomas: a contemporary update. British Journal of Cancer, 125(5), 641–657. https://doi.org/10.1038/s41416-021-01387-w

Skull fractures. (n.d.). Retrieved January 7, 2023, from https:// bestpractice.bmj.com/topics/en-gb/3000207

Song, T. (2019). Generative Model-Based Ischemic Stroke Lesion Segmentation. In arXiv [eess.IV]. arXiv. http://arxiv.org/ abs/1906.02392

Spasov, S., Passamonti, L., Duggento, A., Liò, P., Toschi, N., & Alzheimer’s Disease Neuroimaging Initiative. (2019). A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer’s disease. NeuroImage, 189, 276–287. https://doi.org/10.1016/j. neuroimage.2019.01.031

Strub, W. M., Leach, J. L., Tomsick, T., & Vagal, A. (2007). Overnight preliminary head CT interpretations provided by residents: locations of misidentified intracranial hemorrhage. AJNR. American Journal of Neuroradiology, 28(9), 1679–1682. https://doi.org/10.3174/ajnr.A0653

Tang, Y., Xiao, X., Xie, H., Wan, C.-M., Meng, L., Liu, Z.-H., Liao, W.-H., Tang, B.-S., & Guo, J.-F. (2017). Altered Functional Brain Connectomes between Sporadic and Familial Parkinson’s Patients. Frontiers in Neuroanatomy, 11, 99. https://doi.org/10.3389/fnana.2017.00099

Theocharakis, P., Glotsos, D., Kalatzis, I., Kostopoulos, S., Georgiadis, P., Sifaki, K., Tsakouridou, K., Malamas, M., Delibasis, G., Cavouras, D., & Nikiforidis, G. (2009). Pattern recognition system for the discrimination of multiple sclerosis from cerebral microangiopathy lesions based on texture analysis of magnetic resonance images. Magnetic Resonance Imaging, 27(3), 417–422. https://doi.org/10.1016/j. mri.2008.07.014

Thomalla, G., Cheng, B., Ebinger, M., Hao, Q., Tourdias, T., Wu, O., Kim, J. S., Breuer, L., Singer, O. C., Warach, S., Christensen, S., Treszl, A., Forkert, N. D., Galinovic, I., Rosenkranz, M., Engelhorn, T., Köhrmann, M., Endres, M., Kang, D. W., … Gerloff, C. (2011). DWI-FLAIR mismatch for the identification of patients with acute ischaemic stroke within 4·5 h of symptom onset (PRE-FLAIR): A multicentre observational study. Lancet Neurology, 10(11), 978–986. https:// doi.org/10.1016/S1474-4422(11)70192-2

Thomalla, G., Simonsen, C. Z., Boutitie, F., Andersen, G., Berthezene, Y., Cheng, B., Cheripelli, B., Cho, T.-H., Fazekas, F., Fiehler, J., Ford, I., Galinovic, I., Gellissen, S., Golsari, A., Gregori, J., Günther, M., Guibernau, J., Häusler, K. G., Hennerici, M., … Gerloff, C. (2018). MRI-Guided Thrombolysis for Stroke with Unknown Time of Onset. The New England Journal of Medicine, NEJMoa1804355. https://doi.org/10.1056/ NEJMoa1804355

Thompson, A. J., Banwell, B. L., Barkhof, F., Carroll, W. M., Coetzee, T., Comi, G., Correale, J., Fazekas, F., Filippi, M., Freedman, M. S., Fujihara, K., Galetta, S. L., Hartung, H. P., Kappos, L., Lublin, F. D., Marrie, R. A., Miller, A. E., Miller, D. H., Montalban, X., … Cohen, J. A. (2018). Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurology, 17(2), 162–173. https://doi.org/10.1016/S1474- 4422(17)30470-2

Thust, S. C., van den Bent, M. J., & Smits, M. (2018). Pseudoprogression of brain tumors. Journal of Magnetic Resonance Imaging: JMRI, 48(3), 571–589. https://doi. org/10.1002/jmri.26171

Tommasin, S., Cocozza, S., Taloni, A., Giannì, C., Petsas, N., Pontillo, G., Petracca, M., Ruggieri, S., De Giglio, L., Pozzilli, C., Brunetti, A., & Pantano, P. (2021). Machine learning classifier to identify clinical and radiological features relevant to disability progression in multiple sclerosis. Journal of Neurology, 268(12), 4834–4845. https://doi.org/10.1007/s00415- 021-10605-7

Tsai, J. P., & Albers, G. W. (2017). Wake-Up Stroke. Topics in Magnetic Resonance Imaging: TMRI, 1. https://doi.org/10.1097/ RMR.0000000000000126

Valverde, S., Cabezas, M., Roura, E., González-Villà, S., Pareto, D., Vilanova, J. C., Ramió-Torrentà, L., Rovira, À., Oliver, A., & Lladó, X. (2017). Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach. NeuroImage, 155, 159–168. https://doi.org/10.1016/j.neuroimage.2017.04.034

Valverde, S., Salem, M., Cabezas, M., Pareto, D., Vilanova, J. C., Ramió-Torrentà, L., Rovira, À., Salvi, J., Oliver, A., & Lladó, X. (2019). One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks. NeuroImage. Clinical, 21, 101638. https://doi.org/10.1016/j. nicl.2018.101638

van Asch, C. J., Luitse, M. J., Rinkel, G. J., van der Tweel, I., Algra, A., & Klijn, C. J. (2010). Incidence, case fatality, and functional outcome of intracerebral haemorrhage over time, according to age, sex, and ethnic origin: a systematic review and meta-analysis. Lancet Neurology, 9(2), 167–176. https://doi. org/10.1016/S1474-4422(09)70340-0

van Leeuwen, K. G., Meijer, F. J. A., Schalekamp, S., Rutten, M. J. C. M., van Dijk, E. J., van Ginneken, B., Govers, T. M., & de Rooij, M. (2021). Cost-effectiveness of artificial intelligence aided vessel occlusion detection in acute stroke: an early health technology assessment. Insights into Imaging, 12(1), 133. https://doi.org/10.1186/s13244-021-01077-4

van Leeuwen, K. G., Schalekamp, S., Rutten, M. J. C. M., van Ginneken, B., & de Rooij, M. (2021). Artificial intelligence in radiology: 100 commercially available products and their scientific evidence. European Radiology, 31(6), 3797–3804. https://doi.org/10.1007/s00330-021-07892-z

Wachinger, C., Reuter, M., & Klein, T. (2018). DeepNAT: Deep convolutional neural network for segmenting neuroanatomy. NeuroImage, 170, 434–445. https://doi.org/10.1016/j. neuroimage.2017.02.035

Wang, X., Shen, T., Yang, S., Lan, J., Xu, Y., Wang, M., Zhang, J., & Han, X. (2021). A deep learning algorithm for automatic detection and classification of acute intracranial hemorrhages in head CT scans. NeuroImage. Clinical, 32, 102785. https://doi. org/10.1016/j.nicl.2021.102785

Wardlaw, J. M., Mair, G., von Kummer, R., Williams, M. C., Li, W., Storkey, A. J., Trucco, E., Liebeskind, D. S., Farrall, A., Bath, P. M., & White, P. (2022). Accuracy of Automated Computer-Aided Diagnosis for Stroke Imaging: A Critical Evaluation of Current Evidence. Stroke; a Journal of Cerebral Circulation, 53(7), 2393–2403. https://doi.org/10.1161/ STROKEAHA.121.036204

Wattjes, M. P., Rovira, À., Miller, D., Yousry, T. A., Sormani, M. P., de Stefano, M. P., Tintoré, M., Auger, C., Tur, C., Filippi, M., Rocca, M. A., Fazekas, F., Kappos, L., Polman, C., Frederik Barkhof, Xavier Montalban, & MAGNIMS study group. (2015). Evidence-based guidelines: MAGNIMS consensus guidelines on the use of MRI in multiple sclerosis--establishing disease prognosis and monitoring patients. Nature Reviews. Neurology, 11(10), 597–606. https://doi.org/10.1038/nrneurol.2015.157

Wildner, P., Stasiołek, M., & Matysiak, M. (2020). Differential diagnosis of multiple sclerosis and other inflammatory CNS diseases. Multiple Sclerosis and Related Disorders, 37, 101452. https://doi.org/10.1016/j.msard.2019.101452

Wismüller, A., & Stockmaster, L. (2020). A prospective randomized clinical trial for measuring radiology study reporting time on Artificial Intelligence-based detection of intracranial hemorrhage in emergent care head CT. Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging, 11317, 144–150. https://doi. org/10.1117/12.2552400

Wong, K. K., Cummock, J. S., Li, G., Ghosh, R., Xu, P., Volpi, J. J., & Wong, S. T. C. (2022). Automatic Segmentation in Acute Ischemic Stroke: Prognostic Significance of Topological Stroke Volumes on Stroke Outcome. Stroke; a Journal of Cerebral Circulation, 101161STROKEAHA121037982. https://doi. org/10.1161/STROKEAHA.121.037982

World Stroke Organisation (2022) Global Stroke Fact Sheet. Retrieved December 2022 from https://www.world-stroke.org/ assets/downloads/WSO_Global_Stroke_Fact_Sheet.pdf

Wottschel, V., Alexander, D. C., Kwok, P. P., Chard, D. T., Stromillo, M. L., De Stefano, N., Thompson, A. J., Miller, D. H., & Ciccarelli, O. (2015). Predicting outcome in clinically isolated syndrome using machine learning. NeuroImage. Clinical, 7, 281–287. https://doi.org/10.1016/j.nicl.2014.11.021

Wottschel, V., Chard, D. T., Enzinger, C., Filippi, M., Frederiksen, J. L., Gasperini, C., Giorgio, A., Rocca, M. A., Rovira, A., De Stefano, N., Tintoré, M., Alexander, D. C., Barkhof, F., Ciccarelli, O., & MAGNIMS study group and the EuroPOND consortium. (2019). SVM recursive feature elimination analyses of structural brain MRI predicts nearterm relapses in patients with clinically isolated syndromes suggestive of multiple sclerosis. NeuroImage. Clinical, 24, 102011. https://doi.org/10.1016/j.nicl.2019.102011

Yahav-Dovrat, A., Saban, M., Merhav, G., Lankri, I., Abergel, E., Eran, A., Tanne, D., Nogueira, R. G., & Sivan-Hoffmann, R. (2021). Evaluation of Artificial Intelligence-Powered Identification of Large-Vessel Occlusions in a Comprehensive Stroke Center. AJNR. American Journal of Neuroradiology, 42(2), 247–254. https://doi.org/10.3174/ajnr.A6923

Ye, H., Gao, F., Yin, Y., Guo, D., Zhao, P., Lu, Y., Wang, X., Bai, J., Cao, K., Song, Q., Zhang, H., Chen, W., Guo, X., & Xia, J. (2019). Precise diagnosis of intracranial hemorrhage and subtypes using a three-dimensional joint convolutional and recurrent neural network. European Radiology, 29(11), 6191–6201. https:// doi.org/10.1007/s00330-019-06163-2

Yoo, Y., Tang, L. Y. W., Brosch, T., Li, D. K. B., Kolind, S., Vavasour, I., Rauscher, A., MacKay, A. L., Traboulsee, A., & Tam, R. C. (2018). Deep learning of joint myelin and T1w MRI features in normal-appearing brain tissue to distinguish between multiple sclerosis patients and healthy controls. NeuroImage. Clinical, 17, 169–178. https://doi.org/10.1016/j. nicl.2017.10.015

Zaki, L. A. M., Vernooij, M. W., Smits, M., Tolman, C., Papma, J. M., Visser, J. J., & Steketee, R. M. E. (2022). Comparing two artificial intelligence software packages for normative brain volumetry in memory clinic imaging. Neuroradiology, 64(7), 1359–1366. https://doi.org/10.1007/s00234-022-02898-w

Zhang, Q., Cao, J., Zhang, J., Bu, J., Yu, Y., Tan, Y., Feng, Q., & Huang, M. (2019). Differentiation of Recurrence from Radiation Necrosis in Gliomas Based on the Radiomics of Combinational Features and Multimodality MRI Images. Computational and Mathematical Methods in Medicine, 2019, 2893043. https://doi. org/10.1155/2019/2893043

Zhang, S., Nguyen, T. D., Zhao, Y., Gauthier, S. A., Wang, Y., & Gupta, A. (2018). Diagnostic accuracy of semiautomatic lesion detection plus quantitative susceptibility mapping in the identification of new and enhancing multiple sclerosis lesions. NeuroImage. Clinical, 18, 143–148. https://doi.org/10.1016/j. nicl.2018.01.013

Zhang, Y.-Q., Liu, A.-F., Man, F.-Y., Zhang, Y.-Y., Li, C., Liu, Y.-E., Zhou, J., Zhang, A.-P., Zhang, Y.-D., Lv, J., & Jiang, W.-J. (2022). MRI radiomic features-based machine learning approach to classify ischemic stroke onset time. Journal of Neurology, 269(1), 350–360. https://doi.org/10.1007/s00415-021-10638-y

Zhao, Y., Healy, B. C., Rotstein, D., Guttmann, C. R. G., Bakshi, R., Weiner, H. L., Brodley, C. E., & Chitnis, T. (2017). Exploration of machine learning techniques in predicting multiple sclerosis disease course. PloS One, 12(4), e0174866. https://doi.org/10.1371/journal.pone.0174866

Zhao, Y., Wang, T., Bove, R., Cree, B., Henry, R., Lokhande, H., Polgar-Turcsanyi, M., Anderson, M., Bakshi, R., Weiner, H. L., Chitnis, T., & SUMMIT Investigators. (2020). Ensemble learning predicts multiple sclerosis disease course in the SUMMIT study. NPJ Digital Medicine, 3, 135. https://doi. org/10.1038/s41746-020-00338-8

Zhu, H., Jiang, L., Zhang, H., Luo, L., Chen, Y., & Chen, Y. (2021). An automatic machine learning approach for ischemic stroke onset time identification based on DWI and FLAIR imaging. NeuroImage. Clinical, 31, 102744. https://doi.org/10.1016/j. nicl.2021.102744

Zurita, M., Montalba, C., Labbé, T., Cruz, J. P., Dalboni da Rocha, J., Tejos, C., Ciampi, E., Cárcamo, C., Sitaram, R., & Uribe, S. (2018). Characterization of relapsing-remitting multiple sclerosis patients using support vector machine classifications of functional and diffusion MRI data. NeuroImage. Clinical, 20, 724–730. https://doi.org/10.1016/j. nicl.2018.09.002

Guide to Artificial Intelligence in Radiology

Introduction

Artificial intelligence (AI) is playing a growing role in all our lives and has shown promise in addressing some of the greatest current and upcoming societal challenges we face. The healthcare industry, though notoriously complex and resistant to disruption, potentially has a lot to gain from the use of AI. With an established history of leading digital transformation in healthcare and an urgent need for improved efficiency, radiology has been at the forefront of harnessing AI’s potential.

This book covers how and why AI can address challenges faced by radiology departments, provides an overview of the fundamental concepts related to AI, and describes some of the most promising use cases for AI in radiology. In addition, the major challenges associated with the adoption of AI into routine radiological practice are discussed. The book also covers some crucial points radiology departments should keep in mind when deciding on which AI-based solutions to purchase. Finally, it provides an outlook on what new and evolving aspects of AI in radiology to expect in the near future.

The case for artificial intelligence in radiology

The healthcare industry has experienced a number of trends over the past few decades that demand a change in the way certain things are done. These trends are particularly salient in radiology, where the diagnostic quality of imaging scans has improved dramatically while scan times have decreased. As a result, the amount and complexity of medical imaging data acquired have increased substantially over the past few decades (Smith-Bindman et al., 2019; Winder et al., 2021) and are expected to continue to increase (Tsao, 2020). This issue is complicated by a widespread global shortage of radiologists (AAMC Report Reinforces Mounting Physician Shortage, 2021, Clinical Radiology UK Workforce Census 2019 Report, 2019). Healthcare workers, including radiologists, have an increasing workload (Bruls & Kwee, 2020; Levin et al., 2017) that contributes to burnout and medical errors (Harry et al., 2021). Being an essential service provider to virtually all other hospital departments, staff shortages within radiology have significant effects that spread throughout the hospital and to society as a whole (England & Improvement, 2019; Sutherland et al., n.d.).

With an ageing global population and a rising burden of chronic illnesses, these issues are expected to pose even more of a challenge to the healthcare industry in the future.

AI-based medical imaging solutions have the potential to ameliorate these challenges for several reasons. They are particularly suited to handling large, complex datasets (Alzubaidi et al., 2021). Moreover, they are well suited to automate some of the tasks traditionally performed by radiologists and radiographers, potentially freeing up time and making workflows within radiology departments more efficient (Allen et al., 2021; Baltruschat et al., 2021; Kalra et al., 2020; O’Neill et al., 2021; van Leeuwen et al., 2021; Wong et al., 2019). AI is also capable of detecting complex patterns in data that humans cannot necessarily find or quantify (Dance, 2021; Korteling et al., 2021; Kühl et al., 2020).

Fundamentals of artificial intelligence

The term “artificial intelligence” refers to the use of computer systems to solve specific problems in a way that simulates human reasoning. One fundamental characteristic of AI is that, like humans, these systems can tailor their solutions to changing circumstances. Note that, while these systems are meant to mimic on a fundamental level how humans think, their capacity to do so (e.g. in terms of the amount of data they can handle at one time, the nature and amount of patterns they can find in the data, and the speed at which they do so) often exceeds that of humans.

AI solutions come in the form of computer algorithms, which are pieces of computer code representing instructions to be followed to solve a specific problem. In its most fundamental form, the algorithm takes data as an input, performs some computation on that data, and returns an output.

An AI algorithm can be explicitly programmed to solve a specific task, analogous to a step-by-step recipe for baking a cake. On the other hand, the algorithm can be programmed to look for patterns within the data in order to solve the problem. These types of algorithms are known as machine learning algorithms. Thus, all machine learning algorithms are AI, but not all AI is machine learning. The patterns in the data that the algorithm can be explicitly programmed to look for or that it can “discover” by itself are known as features. An important characteristic of machine learning is that such algorithms learn from the data itself, and their performance improves the more data they are given.

One of the most common uses of machine learning is in classification - assigning a piece of data a particular label. For example, a machine learning algorithm might be used to tell if a photo (the input) shows a dog or a cat (the label). The algorithm can learn to do so in a supervised or unsupervised way.

Supervised learning

In supervised learning, the machine learning algorithm is given data that has been labelled with the ground truth, in this example, photos of dogs and cats that have been labelled as such. The process then goes through the following phases:

1.Training phase: The algorithm learns the features associated with dogs and cats using the aforementioned data (training data).
2.Test phase: The algorithm is then given a new set of photos (the test data), it labels them and the performance of the algorithm on that data is assessed.

In some cases, there is a phase in between training and test, known as the validation phase. In this phase, the algorithm is given a new set of photos (not included in either the training or test data), its performance is assessed on this data, and the model is tweaked and retrained on the training data. This is repeated until some predefined performance-based criterion is reached, and the algorithm then enters the test phase.

Unsupervised learning

In unsupervised learning, the algorithm identifies features within the input data that allow it to assign classes to the individual data points without being told explicitly what those classes are or should be. Such algorithms can identify patterns or group data points together without human intervention and include clustering and dimensionality reduction algorithms. Not all machine learning algorithms perform classification. Some are used to predict a continuous metric (e.g. the temperature in four weeks’ time) instead of a discrete label (e.g. cats vs dogs). These are known as regression algorithms.

Neural networks and deep learning

A neural network is made up of an input layer and an output layer, which are themselves composed of nodes. In simple neural networks, features that are manually derived from a dataset are fed into the input layer, which performs some computations, the results of which are relayed to the output layer. In deep learning, multiple “hidden” layers exist between the input and output layers. Each node of the hidden layers performs calculations using certain weights and relays the output to the next hidden layer until the output layer is reached.

In the beginning, random values are assigned to the weights and the accuracy of the algorithm is calculated. The values of the weights are then iteratively adjusted until a set of weight values that maximize accuracy is found. This iterative adjustment of the weight values is usually done by moving backwards from the output layer to the input layer, a technique called backpropagation. This entire process is done on the training data.

Performance evaluation

Understanding how the performance of AI algorithms is assessed is key to interpreting the AI literature. Several performance metrics exist for assessing how well a model performs certain tasks. No single metric is perfect, so a combination of several metrics provides a fuller picture of model performance.

In regression, the most commonly used metrics include:

Mean absolute error (MAE): the average difference between the predicted values and the ground truth.
Root mean square error (RMSE): the differences between the predicted values and the ground truth are squared and then averaged over the sample. Then the square root of the average is taken. Unlike the MAE, the RMSE thus gives higher weight to larger differences.
R²: the proportion of the total variance in the ground truth explained by the variance in the predicted values. It ranges from 0 to 1.

The following metrics are commonly used in classification tasks:

Accuracy: this is the proportion of all predictions that were predicted correctly. It ranges from 0 to 1.
Sensitivity: also known as the true positive rate (TPR) or recall, this is the proportion of true positives that were predicted correctly. It ranges from 0 to 1.
Specificity: Also known as the true negative rate (TNR), this is the proportion of true negatives that were predicted correctly. It ranges from 0 to 1.
Precision: also known as positive predictive value (PPV), this is the proportion of positive classifications that were predicted correctly. It ranges from 0 to 1.

An inherent trade-off exists between sensitivity and specificity. The relevant importance of each, as well as their interpretation, highly depends on the specific research question and classification task.

Importantly, although classification models are meant to reach a binary conclusion, they are inherently probability-based. This means that these models will output a probability that a data point belongs to one class or another. In order to reach a conclusion on the most likely class, a threshold is used. Metrics such as accuracy, sensitivity, specificity and precision refer to the performance of the algorithm based on a certain threshold. The area under the receiver operating characteristic curve (AUC) is a threshold-independent performance metric. The AUC can be interpreted as the probability that a random positive example is ranked higher by the algorithm than a random negative example.

In image segmentation tasks, which are a type of classification task, the following metrics are commonly used:

Dice similarity coefficient: a measure of overlap between two sets (e.g. two images) that is calculated as two times the number of elements common to the sets divided by the sum of the number of elements in each set. It ranges from 0 (no overlap) to 1 (perfect overlap).
Hausdorff distance: a measure of how far two sets (e.g. two images) within a space are far from each other. It is basically the largest distance from one point in one set to the closest point in the other set.

Internal and external validity

Internally valid models perform well in their task on the data being used to train and validate them. The degree to which they are internally valid is assessed using the performance metrics outlined above and depends on the characteristics of the model itself and the quality of the data that the model was trained and validated on.

Externally valid models perform well in their tasks on new data (Ramspek et al., 2021). The better the model performs on data that differs from the data the models were trained and validated on, the higher the external validity. In practice, this often requires the performance of the models to be tested on data from hospitals or geographical areas that were not part of the model’s training and validation datasets.

Guidelines for evaluating AI research

Several guidelines have been developed to assess the evidence behind AI-based interventions in healthcare (X. Liu et al., 2020; Mongan et al., 2020; Shelmerdine et al., 2021; Weikert et al., 2021). These provide a template for those doing AI research in healthcare and ensure that relevant information is reported transparently and comprehensively, but can also be used by other stakeholders to assess the quality of published research. This helps ensure that AI-based solutions with substantial potential or actual limitations, particularly those caused by poor reporting (Bozkurt et al., 2020; D. W. Kim et al., 2019; X. Liu et al., 2019; Nagendran et al., 2020; Yusuf et al., 2020), are not prematurely adopted (CONSORT-AI and SPIRIT-AI Steering Group, 2019). Guidelines have also been proposed for evaluating the trustworthiness of AI-based solutions in terms of transparency, confidentiality, security, and accountability (Buruk et al., 2020; Lekadir et al., 2021; Zicari et al., 2021).

Clinical uses

Over the past few years, AI has shown great potential in addressing a broad range of tasks within a medical imaging department, including many that happen before the patient is scanned. Implementations of AI to improve the efficiency of radiology workflows prior to patient scanning are sometimes referred to as “upstream AI” (Kapoor et al., 2020; M. L. Richardson et al., 2021).

Scheduling

One promising upstream AI application is predicting whichpatients arelikelytomisstheirscanappointments. Missed appointments are associated with significantly increased workload and costs (Dantas et al., 2018). Using a Gradient Boosting approach, Nelson et al. predicted missed hospital magnetic resonance imaging (MRI) appointments in the United Kingdom’s National Health Service (NHS) with high accuracy (Nelson et al., 2019). Their simulations also suggested that acting on the predictions of this model by targeting patients who are likely to miss their appointments would potentially yield a net benefit of several pounds per appointment across a range of model thresholds and missed appointment rates (Nelson et al., 2019). Similar results were recently found in a study of a single hospital in Singapore. For the 6-month period following the deployment of the predictive tool they were able to significantly reduce the no show rate from 19.3 % tp 15.9 % which translated into a potential economic benefit of $180,000 (Chong et. al., 2020).

Scheduling scans in a radiology department is a challenging endeavour because, although it is largely an administrative task, it depends heavily on medical information. The task of assigning patients to specific appointments thus often requires the input of someone with domain knowledge, which stipulates that either the person making the appointments must be a radiologist or radiology technician, or these people will have to provide input regularly. In either scenario, the process is somewhat inefficient and can potentially be streamlined using AI-based algorithms that check scan indications and contraindications and provide the people scheduling the scans with information about scan urgency (Letourneau-Guillon et al., 2020).

Protocolling

Depending on hospital or clinic policy, the decision on what exact scan protocol a patient receives is usually made based on the information on the referring physician’s scan request and the judgement of the radiologist. This is often supplemented by direct communication between the referring physician and radiologist and the radiologist’s review of the patient’s medical information. This process improves patient care (Boland et al., 2014) but can be time-consuming and inefficient, particularly with modalities like MRI, where a large number of protocol permutations exist. In one study, protocolling alone accounted for about 6 % of the radiologist’s working time (Schemmel et al., 2016). Radiologists are also often interrupted by tasks such as protocolling when interpreting images, despite the fact that the latter is considered a radiologist’s primary responsibility (Balint et al., 2014; J.-P. J. Yu et al., 2014).

Interpretation of the narrative text of the referring physician’s scan request has been attempted using natural language classifiers, the same technology used in chatbots and virtual assistants. Natural language classifiers based on deep learning have shown promise in assigning patients to either a contrast-enhanced or non-enhanced MRI protocol for musculoskeletal MRI, with an accuracy of 83 % (Trivedi et al., 2018) and 94 % (Y. H. Lee, 2018). Similar algorithms have shown an accuracy of 95 % for predicting the appropriate brain MRI protocol using a combination of up to 41 different MRI sequences (Brown & Marotta, 2018). Across a wide range of body regions, a deep-learning-based natural language classifier decided based on the narrative text of the scan requests whether to automatically assign a specific computed tomography (CT) or MRI protocol (which it did with 95 % accuracy) or, in more difficult cases, recommend a list of three most appropriate protocols to the radiologist (which it did with 92 % accuracy) (Kalra et al., 2020).

AI has also been used to decide whether already protocolled scans need to be extended, a decision which has to be made in real-time while the patient is inside the scanner. One such example is in prostate MRI, where a decision on whether to administer a contrast agent is often made after the non-contrast sequences. Hötker et al. found that a convolutional neural network (CNN) assigned 78 % of patients to the appropriate prostate MRI protocol (Hötker et al., 2021). The sensitivity of the CNN for the need for contrast was 94.4 % with a specificity of 68.8 % and only 2 % of patients in their study would have had to be called back for a contrast- enhanced scan (Hötker et al., 2021).

Image quality improvement and monitoring

Many AI-based solutions that work in the background of radiology workflows to improve image quality have recently been established. These include solutions for monitoring image quality, reducing image artefacts, improving spatial resolution, and speeding up scans.

Such solutions are entering the radiology mainstream, particularly for computed tomography, which for decades used established but artefact-prone methods for reconstructing interpretable images from the raw sensor data (Deák et al., 2013; Singh et al., 2010).

These are gradually being replaced by deep-learning- based reconstruction methods, which improve image quality while maintaining low radiation doses (Akagi et al., 2019; H. Chen et al., 2017; Choe et al., 2019; Shan et al., 2019). This reconstruction is performed on supercomputers on the CT scanner itself or on the cloud. The balance between radiation dose and image quality can be adjusted on a protocol-specific basis to tailor scans to individual patients and clinical scenarios (McLeavy et al., 2021; Willemink & Noël, 2019). Such approaches have found particular use when scanning children, pregnant women, and obese patients as well as CT scans of the urinary tract and heart (McLeavy et al., 2021).

AI-based solutions have also been used to speed up scans while maintaining diagnostic quality. Scan time reduction not only improves overall efficiency but also contributes to an overall better patient experience and compliance with imaging examination. A multi- centre study of spine MRI showed that a deep-learning- based image reconstruction algorithm that enhanced images using filtering and detail-preserving noise reduction reduced scan times by 40 % (Bash, Johnson, et al., 2021). For T1-weighted MRI scans of the brain, a similar algorithm that improves image sharpness and reduces image noise reduced scan times by 60 % while maintaining the accuracy of brain region volumetry compared to standard scans (Bash, Wang, et al., 2021).

In routine radiological practice, images often contain artefacts that reduce their interpretability. These artefacts are the result of characteristics of the specific imaging modality or protocol used or factors intrinsic to the patient being scanned, such as the presence of foreign bodies or the patient moving during the scan. Particularly with MRI, imaging protocols that demand fast scanning often introduce certain artefacts to the reconstructed image. In one study, a deep-learning- based algorithm reduced banding artefacts associated with balanced steady-state free precession MRI sequences of the brain and knee (K. H. Kim & Park, 2017). For real-time imaging of the heart using MRI, another study found that the aliasing artefacts introduced by the data undersampling were reduced by using a deep-learning-based approach (Hauptmann et al., 2019). The presence of metallic foreign bodies such as dental, orthopaedic or vascular implants is a common patient-related factor causing image artefacts in both CT and MRI (Boas & Fleischmann, 2012; Hargreaves et al., 2011). Although not yet well established, several deep-learning-based approaches for reducing these artefacts have been investigated (Ghani & Clem Karl, 2019; Puvanasunthararajah et al., 2021; Zhang & Yu, 2018). Similar approaches are being tested for reducing motion-related artefacts in MRI (Tamada et al., 2020; B. Zhao et al., 2022).

AI-based solutions for monitoring image quality potentially reduce the need to call patients back to repeat imaging examinations, which is a common problem (Schreiber-Zinaman & Rosenkrantz, 2017). A deep-learning-based algorithm that identifies the radiographic view acquired and extracts quality-related metrics from ankle radiographs was able to predict image quality with about 94 % accuracy (Mairhöfer et al., 2021). Another deep-learning-based approach was capable of predicting nondiagnostic liver MRI scans with a negative predictive value of between 86 % and 94 % (Esses et al., 2018). This real-time automated quality control potentially allows radiology technicians to rerun scans or run additional scans with greater diagnostic value.

Scan reading prioritization

With staff shortages and increasing scan numbers, radiologists face long reading lists. To optimize efficiency and patient care, AI-based solutions have been suggested as a way to prioritize which scans radiologists read and report first, usually by screening acquired images for findings that require urgent intervention (O’Connor & Bhalla, 2021). This has been most extensively studied in neuroradiology, where moving CT scans that were found to have intracranial haemorrhage by an AI-based tool to the top of the reading list reduced the time it took radiologists to view the scans by several minutes (O’Neill et al., 2021). Another study found that the time-to diagnosis (which includes the time from image acquisition to viewing by the radiologist and the time to read and report the scans) was reduced from 512 to 19 minutes in an outpatient setting when such a worklist prioritization was used (Arbabshirani et al., 2018). A simulation study using AI-based worklist prioritization based on identifying urgent findings on chest radiographs (such as pneumothorax, pleural effusions, and foreign bodies) also found a substantial reduction in the time it took to view and report the scans compared to standard workflow prioritization (Baltruschat et al., 2021).

Image interpretation

Currently, the majority of commercially available AI- based solutions in medical imaging focus on some aspect of analyzing and interpreting images (Rezazade Mehrizi et al., 2021; van Leeuwen et al., 2021). This includes segmenting parts of the image (for surgical or radiation therapy targeting, for example), bringing suspicious areas to radiologists’ attention, extracting imaging biomarkers (radiomics), comparing images across time, and reaching specific imaging diagnoses.

Neurology

¡ 29–38 % of commercially available AI-based applications in radiology (Rezazade Mehrizi et al., 2021; van Leeuwen et al., 2021).

Most commercially available AI-based solutions targeted at neuroimaging data aim to detect and characterize ischemic stroke, intracranial haemorrhage, dementia, and multiple sclerosis (Olthof et al., 2020). Several studies have shown excellent accuracy of AI- based methods for the detection and classification of intraparenchymal, subarachnoid, and subdural haemorrhage on head CT (Flanders et al., 2020; Ker et al., 2019; Kuo et al., 2019). Subsequent studies showed that, compared to radiologists, some AI-based solutions have substantially lower false positive and negative rates (Ginat, 2020; Rao et al., 2021). In ischemic stroke, AI-based solutions have largely focused on the quantification of the infarct core (Goebel et al., 2018; Maegerlein et al., 2019), the detection of large vessel occlusion (Matsoukas et al., 2022; Morey et al., 2021; Murray et al., 2020; Shlobin et al., 2022), and the prediction of stroke outcomes (Bacchi et al., 2020; Nielsen et al., 2018; Y. Yu et al., 2020, 2021).

In multiple sclerosis, AI has been used to identify and segment lesions (Nair et al., 2020; S.-H. Wang et al., 2018), which can be particularly helpful for the longitudinal follow-up of patients. It has also been used to extract imaging features associated with progressive disease and conversion from clinically isolated syndrome to definite multiple sclerosis (Narayana et al., 2020; Yoo et al., 2019). Other applications of AI in neuroradiology include the detection of intracranial aneurysms (Faron et al., 2020; Nakao et al., 2018; Ueda et al., 2019) and the segmentation of brain tumours (Kao et al., 2019; Mlynarski et al., 2019; Zhou et al., 2020) as well as the prediction of brain tumour genetic markers from imaging data (Choi et al., 2019; J. Zhao et al., 2020)

Chest

¡ 24 %–31 % of commercially available AI-based applications in radiology (Rezazade Mehrizi et al., 2021; van Leeuwen et al., 2021).

When interpreting chest radiographs, radiologists detected substantially more critical and urgent findings when aided by a deep-learning-based algorithm, and did so much faster than without the algorithm (Nam et al., 2021). Deep-learning-based image interpretation algorithms have also been found to improve radiology residents’ sensitivity for detecting urgent findings on chest radiographs from 66 % to 73 % (E. J. Hwang, Nam, et al., 2019). Another study which focused on a broader range of findings on chest radiographs also found that radiologists aided by a deep-learning-based algorithm had higher diagnostic accuracy than radiologists who read the radiographs without assistance (Seah et al., 2021). The uses of AI in chest radiology also extend to cross-sectional imaging like CT. A deep learning algorithm was found to detect pulmonary embolism on CT scans with high accuracy (AUC = 0.85) (Huang, Kothari, et al., 2020). Moreover, a deep learning algorithm was 90 % accurate in detecting aortic dissection on non-contrast-enhanced CT scans, similar to the performance of radiologists (Hata et al., 2021).

Outside the emergency setting, AI-based solutions have been widely tested and implemented for tuberculosis screening on chest radiographs (E. J. Hwang, Park, et al., 2019; S. Hwang et al., 2016; Khan et al., 2020; Qin et al., 2019; WHO Operational Handbook on Tuberculosis Module 2: Screening – Systematic Screening for Tuberculosis Disease, n.d.). In addition, they have been useful for lung cancer screening both in terms of detecting lung nodules on CT (Setio et al., 2017) and chest radiographs (Li et al., 2020) and by classifying whether nodules are likely to be malignant or benign (Ardila et al., 2019; Bonavita et al., 2020; Ciompi et al., 2017; B. Wu et al., 2018). AI-based solutions also show great promise for the diagnosis of pneumonia, chronic obstructive pulmonary disease, and interstitial lung disease (F. Liu et al., 2021).

Breast

¡ 11 % of commercially available AI-based applications in radiology (Rezazade Mehrizi et al., 2021; van Leeuwen et al., 2021).

So far, many of the AI-based algorithms targeting breast imaging aim to reduce the workload of radiologists reading mammograms. Ways to do this have included using AI-based algorithms to triage out negative mammograms, which in one study was associated with a reduction in radiologists’ workload by almost one-fifth (Yala et al., 2019). Other studies that have replaced second readers of mammograms with AI- based algorithms have shown that this leads to fewer false positives and false negatives as well as reduces the workload of the second reader by 88 % (McKinney et al., 2020).

AI-based solutions for mammography have also been found to increase the diagnostic accuracy of radiologists (McKinney et al., 2020; Rodríguez-Ruiz et al., 2019; Watanabe et al., 2019) and some have been found to be highly accurate in independently detecting and classifying breast lesions (Agnes et al., 2019; Al- Antari et al., 2020; Rodriguez-Ruiz et al., 2019).
Despite this, a recent systematic review of 36 AI- based algorithms found that these studies were of poor methodological quality and that all algorithms were less accurate than the consensus of two or more radiologists (Freeman et al., 2021). AI-based algorithms have nonetheless shown potential for extracting cancer-predictive features from mammograms beyond mammographic breast density (Arefan et al., 2020; Dembrower et al., 2020; Hinton et al., 2019). Beyond mammography, AI-based solutions have been developed for detecting and classifying breast lesions on ultrasound (Akkus et al., 2019; Park et al., 2019; G.- G. Wu et al., 2019) and MRI (Herent et al., 2019).

Cardiac

¡ 11 % of commercially available AI-based applications in radiology (Rezazade Mehrizi et al., 2021; van Leeuwen et al., 2021).

Cardiac radiology has always been particularly challenging because of the difficulties inherent in acquiring images of a constantly moving organ. Because of this, it has benefited immensely from advances in imaging technology and seems set to benefit greatly from AI as well (Sermesant et al., 2021). Most of the AI-based applications of the cardiovascular system use MRI, CT or ultrasound data (Weikert et al., 2021). Prominent examples include the automated calculation of ejection fraction on echocardiography, quantification of coronary artery calcification on cardiac CT, determination of right ventricular volume on CT pulmonary angiography, and determination of heart chamber size and thickness on cardiac MRI (Medical AI Evaluation, n.d., The Medical Futurist, n.d.). AI-based solutions for the prediction of patients likely to respond favourably to cardiac interventions, such as cardiac resynchronization therapy, based on imaging and clinical parameters have also shown great promise (Cikes et al., 2019; Hu et al., 2019). Changes in cardiac MRI not readily visible to human readers but potentially useful for differentiating different types of cardiomyopathies can also be detected using AI through texture analysis (Neisius et al., 2019; J. Wang et al., 2020) and other radiomic approaches (Mancio et al., 2022).

Musculoskeletal

¡ 7–11 % of commercially available AI-based applications in radiology (Rezazade Mehrizi et al., 2021; van Leeuwen et al., 2021).

Promising applications of AI in the assessment of muscles, bones and joints include applications where human readers generally show poor between- and within-rater reliability, such as the determination of skeletal age based on bone radiographs (Halabi et al., 2019; Thodberg et al., 2009) and screening for osteoporosis on radiographs (Kathirvelu et al., 2019; J.-S. Lee et al., 2019) and CT (Pan et al., 2020). AI- based solutions have also shown promise for detecting fractures on radiographs and CT (Lindsey et al., 2018; Olczak et al., 2017; Urakawa et al., 2019). One systematic review of AI-based solutions for fracture detection in several different body parts showed AUCs ranging from 0.94 to 1.00 and accuracies of 77 % to 98 % (Langerhuizen et al., 2019). AI-based solutions have also achieved accuracies similar to radiologists for classification of the severity of degenerative changes of the spine (Jamaludin et al., 2017) and extremity joints (F. Liu et al., 2018; Thomas et al., 2020). AI-based solutions have also been developed to determine the origin of skeletal metastases (Lang et al., 2019) and the classification of primary bone tumours (Do et al., 2017).

Abdomen and pelvis

¡ 4 % of commercially available AI-based applications in radiology (Rezazade Mehrizi et al., 2021; van Leeuwen et al., 2021).

Much of the efforts in using AI in abdominal imaging have thus far concentrated on the automated segmentation of organs such as the liver (Dou et al., 2017), spleen (Moon et al., 2019), pancreas (Oktay et al., 2018), and kidneys (Sharma et al., 2017). In addition, a systematic review of 11 studies using deep learning for the detection of malignant liver masses showed accuracies of up to 97 % and AUCs of up to 0.92 (Azer, 2019).

Other applications of AI in abdominal radiology include the detection of liver fibrosis (He et al., 2019; Yasaka et al., 2018), fatty liver disease, hepatic iron content, the detection of free abdominal gas on CT, and automated volumetry and segmentation of the prostate (AI for Radiology, n.d.).

Obstacles to implementation

Despite the great potential of AI in medical imaging, it has yet to find widespread implementation and impact in routine clinical practice. This research-to- clinic translation is being hindered by several complex and interrelated issues that directly or indirectly lower the likelihood of AI-based solutions being adopted. One major way they do so is by creating a lack of trust in AI- based solutions by key stakeholders such as regulators, healthcare professionals and patients (Cadario et al., 2021; Esmaeilzadeh, 2020; J. P. Richardson et al., 2021; Tucci et al., 2022).

Generalizability

One major challenge is to develop AI-based solutions that continue to perform well in new, real-world scenarios. In a large systematic review, almost half of the studied AI-based medical imaging algorithms reported a greater than 0.05 decrease in the AUC when tested on new data (A. C. Yu et al., 2022). This lack of generalizability can lead to adverse effects on how well the model performs in a real-world scenario.

If a solution performs poorly when tested on a dataset with a similar or identical distribution to the training dataset, it is said to lack narrow generalizability and is often a consequence of overfitting (Eche et al., 2021). Potential solutions for overfitting are using larger training datasets and reducing the model’s complexity. If a solution performs poorly when tested on a dataset with a different distribution to the training dataset (e.g. a different distribution of patient ethnicities), it is said to lack broad generalizability (Eche et al., 2021). Solutions to poor broad generalizability include stress-testing the model on datasets with different distributions from the training dataset (Eche et al., 2021).

AI solutions are often developed in a high-resource environment such as large technology companies and academic medical centres in wealthy countries. It is likely that findings and performance in these high-resource contexts will fail to generalize to lower- resource contexts such as smaller hospitals, rural areas or poorer countries (Price & Nicholson, 2019), which complicates the issue further.

Risk of bias

Biases can arise in AI-based solutions due to data or human factors. The former occurs when the data used to train the AI solution does not adequately represent the target population. Datasets can be unrepresentative when they are too small or have been collected in a way that misrepresents a certain population category. AI solutions trained on unrepresentative data perpetuate biases and perform poorly in the population categories underrepresented or misrepresented in the training data. The presence of such biases has been empirically shown in many AI-based medical imaging studies (Larrazabal et al., 2020; Seyyed-Kalantari et al., 2021).

AI-based solutions are prone to several subjective and sometimes implicitly or explicitly prejudiced decisions during their development by humans. These human factors include how the training data is selected, how it is labelled, and how the decision is made to focus on the specific problem the AI-based solution intends to solve (Norori et al., 2021). Some recommendations and tools are available to help minimize the risk of bias in AI research (AIF360: A Comprehensive Set of Fairness Metrics for Datasets and Machine Learning Models, Explanations for These Metrics, and Algorithms to Mitigate Bias in Datasets and Models, n.d., IBM Watson Studio - Model Risk Management, n.d.; Silberg & Manyika, 2019).

Data quantity, quality and variety

Problems such as bias and lack of generalizability can be mitigated by ensuring that training data is of sufficient quantity, quality and variety. However, this is difficult to do because patients are often reluctant to share their data for commercial purposes (Aggarwal, Farag, et al., 2021; Ghafur et al., 2020; Trinidad et al., 2020), hospitals and clinics are usually not equipped to make this data available in a useable and secure manner, and organizing and labelling the data is time- consuming and expensive.

Many datasets can be used for a number of different purposes, and sharing data between companies can help make the process of data collection and organization more efficient, as well as increase the amount of data available for each application. However, developers are often reluctant to share data with each other, or even reveal the exact source of their data, to stay competitive.

Data protection and privacy

The development and implementation of AI-based solutions require that patients are explicitly informed about, and give their consent to, the use of their data for a particular purpose and by certain people. This data also has to be adequately protected from data breaches and misuse. Failure to ensure this greatly undermines the public’s trust in AI-based solutions and hinders their adoption. While regulations governing health data privacy state that the collection of fully anonymized data does not require explicit patient consent (General Data Protection Regulation (GDPR) – Official Legal Text, 2016; Office for Civil Rights (OCR), 2012) and in theory protects from the data being misused, whether or not imaging data can be fully anonymized is controversial (Lotan et al., 2020; Murdoch, 2021). Whether consent can be truly informed considering the complexity of the data being acquired, and the resulting myriad of potential future uses of the data, is also disputed (Vayena & Blasimme, 2017).

IT infrastructure

Among hospital departments, radiology has always been at the forefront ofdigitalization. AI-based solutions that focus on image processing and interpretation are likely to find the prerequisite infrastructure in most radiology departments, for example for linking imaging equipment to computers for analysis and for archiving images and other outputs. However, most radiology departments are likely to require significant infrastructure upgrades for other applications of AI, particularly those requiring the integration of information from multiple sources and having complex outputs. Moreover, it is important to keep in mind that the distribution of necessary infrastructure is highly unequal across and within countries (Health Ethics & Governance, 2021).

In terms of computing power, radiology departments will either have to invest resources into the hardware and personnel necessary to run these AI-based solutions or opt for cloud-based solutions. The former comes with an extra cost but allows data processing within the confines of the hospital or clinic’s local network. Cloud-based solutions for computing (known as “infrastructure as a service” or “IaaS”) are often considered the less secure and less trustworthy option, but this depends on a number of factors and is thus not always true (Baccianella & Gough, n.d.). Guidelines on what to consider when procuring cloud-based solutions in healthcare are available (Cloud Security for Healthcare Services, 2021).

Lack of standardization, interoperability, and integrability

The problem of infrastructure becomes even more complicated when considering how fragmented the AI medical imaging market currently is (Alexander et al., 2020). It is therefore likely that in the near future a single department will have several dozen AI-based solutions from different vendors running simultaneously. Having a separate self-contained infrastructure (e.g. a workstation or server) for each of these would be incredibly complicated and difficult to manage. Suggested solutions for this have included AI solution “marketplaces”, similar to app stores (Advanced AI Solutions for Radiology, n.d., Curated Marketplace, 2018, Imaging AI Marketplace - Overview, n.d., Sectra Amplifier Marketplace, 2021, The Nuance AI Marketplace for Diagnostic Imaging, n.d.), and development of an overarching vendor-neutral infrastructure (Leiner et al., 2021). The successful implementation of such solutions requires close partnerships between AI solution developers, imaging vendors and information technology companies.

Interpretability

It is often impossible to understand exactly how AI- based solutions come to their conclusions, particularly with complex approaches like deep learning. This reduces how transparent the decision-making process for procuring and approving these solutions can be, makes the identification of biases difficult, and makes it harder for clinicians to explain the outputs of these solutions to their patients and to determine whether a solution is working properly or has malfunctioned (Char et al., 2018; Reddy et al., 2020; Vayena et al., 2018; Whittlestone et al., 2019). Some have suggested that techniques that help humans understand how AI- based algorithms made certain decisions or predictions (“interpretable” or “explainable” AI) might help mitigate these challenges. However, others have argued that currently available techniques are unsuitable for understanding individual decisions of an algorithm and have warned against relying on them for ensuring that algorithms work in a safe and reliable way (Ghassemi et al., 2021).

Liability

In healthcare systems, a framework of accountability ensures that healthcare workers and medical institutions can be held responsible for adverse effects resulting from their actions. The question of who should be held accountable for the failures of an AI- based solution is complicated. For pharmaceuticals, for example, the accountability for inherent failures in the product or its use often lies with either the manufacturer or the prescriber. One key difference is that AI-based systems are continuously evolving and learning, and so inherently work in a way that is independent of what their developers could have foreseen (Yeung, 2018). To the end-user such as the healthcare worker, the AI- based solution may be opaque and so they may not be able to tell when the solution is malfunctioning or inaccurate (Habli et al., 2020; Yeung, 2018).

Brittleness

Despite substantial progress in their development over the past few years, deep learning algorithms are still surprising brittle. This means that, when the algorithm faces a scenario that differs substantially from what it faced during training, it cannot contextualize and often produces nonsensical or inaccurate results. This happens because, unlike humans, most algorithms learn to perceive things within the confines of certain assumptions, but fail to generalize outside these assumptions. As an example of how this can be abused with malicious intent, subtle changes to medical images, imperceptible by humans, can render the results of disease-classifying algorithms inaccurate (Finlayson et al., 2018). The lack of interpretability of many AI-based solutions compounds this problem because it makes it difficult to troubleshoot how they reached the wrong conclusion.

Making purchasing decisions

So far, more than 100 AI-based products have gained conformité européenne (CE) marking or Food and Drug Adminstration (FDA) clearance. These products can be found in continuously updated and searchable online databases curated by the FDA (Center for Devices & Radiological Health, n.d.), the American College of Radiology (Assess-AI, n.d.), and others (AI for Radiology, n.d., The Medical Futurist, n.d.; E. Wu et al., 2021). The increasing number of available products, the inherent complexity of many of these solutions, and the fact that many people who usually make purchasing decisions in hospitals are not familiar with evaluating such products make it important to think carefully when deciding on which product to purchase. Such decisions will need to be made after incorporating input from healthcare workers, information technology (IT) professionals, as well as management, finance, legal, and human resources professionals within hospitals.

Deciding on whether to purchase an AI-based solution in radiology, as well as which of the increasing number of commercially available solutions to purchase, includes considerations of quality, safety, and finances. Over the past few years, several guidelines have emerged to help potential buyers make these decisions (A Buyer’s Guide to AI in Health and Care, 2020; Omoumi et al., 2021; Reddy et al., 2021), and these guidelines are likely to evolve in the future with changing expectations from customers, regulatory bodies, and stakeholders involved in reimbursement decisions.

First of all, it has to be clear to the potential buyer what the problem is and whether AI is the appropriate approach to this solution, or whether alternatives exist that are more advantageous on balance. If AI is the appropriate approach, buyers should know exactly what a potential AI-based product’s scope of the solution is - i.e. what specific problem the AI-based solution is designed to solve and in what specific circumstances. This includes whether the solution is intended for screening, diagnosis, monitoring, treatment recommendation or another application. It also includes the intended users of the solution and what kind of specific qualifications or training they are expected to have in order to be able to operate the solution and interpret its outputs. It needs to be clear to buyers whether the solution is intended to replace certain tasks that would normally be performed by the end-user, act as a double-reader, as a triaging mechanism, or for other tasks like quality control. Buyers should also understand whether the solution is intended to provide “new” information (i.e. information that would otherwise be unavailable to the user without the solution), improve the performance of an existing task beyond a human’s or other non-AI-based solution’s performance or if it is intended to save time or other resources.

Buyers should also have access to information that allows them to assess the potential benefits of the AI solution, and this should be backed up by published scientific evidence for the efficacy and cost-efficiency of the solution. How this is done will depend highly on the solution itself and the context in which it is expected to be deployed, but guidelines for this are available (National Institute for Health and Care Excellence (NICE), n.d.). Some questions to ask here would be: How much of an influence will the solution have on patient management? Will it improve diagnostic performance? Will it save time and money? Will it affect patients’ quality of life? It should also be clear to the buyer who exactly is expected to benefit from the use of this solution (Radiologists? Clinicians? Patients? The healthcare system or society as a whole?).

As with any healthcare intervention, all AI-based solutions come with potential risks, and these should be made clear to the buyer. Some of these risks might have legal consequences, such as the potential for misdiagnosis. These risks should be quantified, and potential buyers should have a framework for dealing with them, including identifying a framework for accountability within the organizations implementing these solutions. Buyers should also ensure they clearly understand the potential negative effects on radiologists’ training and the potential disruption to radiologists’ workflows associated with the use of these solutions.

Specifics of the AI solution’s design are also relevant to the decision on whether or not to purchase it. These include how robust the solution is to differences between vendors and scanning parameters, the circumstances under which the algorithm was trained (including potential confounding factors), and the way that performance was assessed. It should also be clear to buyers if and how potential sources of bias were accounted for during development. Because a core characteristic of AI-based solutions is their ability to continuously learn from new data, whether and how exactly this retraining is incorporated into the solution with time should also be clear to the buyer, including whether or not new regulatory approval is needed with each iteration. This also includes whether or not retraining is required, for example, due to changes in imaging equipment at the buyer’s institution.

The main selling points of many AI-based solutions are ease-of-use and improved workflows. Therefore, potential buyers should carefully scrutinize how these solutions are to be integrated into existing workflows, including inter-operability with PACS and electronic medical record systems. Whether or not the solution requires extra hardware (e.g. graphical processing units) or software (e.g. for visualization of the solution’s outputs), or if it can readily be integrated into the existing information technology infrastructure of the buyer’s organization influences the overall cost of the solution for the buyer and is therefore also a critical consideration. In addition, the degree of manual interaction required, both under normal circumstances and for troubleshooting, should be known to the buyer. All potential users of the AI solution should be involved in the purchasing process to ensure that they are familiar with it and that it meets their professional ethical standards and suits their needs.

From a regulatory perspective, it should be clear to the buyer whether the solution complies with medical device and data protection regulations. Has the solution been approved in the buyer’s country? If so, under which risk classification? Buyers should also consider creating data flow maps that display how the data flows in the operation of the AI-based solution, including who has access to the data.

Finally, there are other factors to consider which are not necessarily unique to AI-based solutions and which buyers might be familiar with from purchasing other types of solutions. This includes the licensing model of the solution, how users are to be trained on using the solution, how the solution is maintained, how failures in the solution are dealt with, and whether additional costs are to be expected when scaling up the solution’s implementation (e.g. using the solution for more imaging equipment or more users). This allows the potential buyer to anticipate the current and future costs of purchasing the solution.

Future trends

The past decade of increasing interest and progress in AI-based solutions for medical imaging has set the stage for a number of trends that are likely to appear or intensify in the near future.

Firstly, there is an increasing sentiment that, although AI holds a great deal of promise for interpretive applications (such as the detection of pathology), non-interpretive AI-based solutions might hold the most potential in terms of instilling efficiency into radiology workflows and improving patient experiences. This trend towards involving AI earlier in the patient management process is likely to extend to AI increasingly acting as a clinical decision support system to guide when and which imaging scans are performed.

For this to happen, AI needs to be integrated into existing clinical information systems, and the specific algorithms used need to be able to handle more varied data. This will likely pave the way for the development of algorithms that are capable of integrating demographic, clinical, and laboratory patient data to make recommendations about patient management (Huang, Pareek, et al., 2020; Rockenbach, 2021). The previously mentioned natural language processing algorithms that have been used to interpret scan requests may be useful candidates for this.

In addition, we are likely to see AI algorithms that can interpret multiple different types of imaging data from the same patient. Currently, less than 5 % of commercially available AI-based solutions in medical imaging work with more than one imaging modality (Rezazade Mehrizi et al., 2021; van Leeuwen et al., 2021) despite the fact that the typical patient in a hospital receives multiple imaging scans during their stay (Shinagare et al., 2014). With this, it is also likely that more AI-based solutions will be developed that target hitherto neglected modalities such as nuclear imaging techniques and ultrasound.

The current market for AI-based solutions in radiology is spread across a relatively large number of companies (Alexander et al., 2020). Potential users are likely to expect a streamlined integration of these products in their workflows, which can be challenging in such a fragmented market. Improved integration can be achieved in several different ways, including with vendor-neutral marketplaces or by the gradual consolidation of providers of AI-based solutions.

With the expanding use of AI, the issue of trust between AI developers, healthcare professionals, regulators, and patients will become more relevant. It is therefore likely that efforts will intensify to take steps towards strengthening that trust. This will potentially include raising the expected standards of evidence for AI- based solutions (Aggarwal, Sounderajah, et al., 2021; X. Liu et al., 2019; van Leeuwen et al., 2021; Yusuf et al., 2020), making them more transparent through the use and improvement of interpretable AI techniques (Holzinger et al., 2017; Reyes et al., 2020; “Towards Trustable Machine Learning,” 2018), and enhancing techniques for maintaining patient data privacy (G. Kaissis et al., 2021; G. A. Kaissis et al., 2020).

Furthermore, while most existing regulations stipulate that AI-based algorithms cannot be modified after regulatory approval, this is likely to change in the future. The potential for these algorithms to learn from data acquired after approval and adapt to changing circumstances is a major advantage of AI. Still, frameworks for doing so have thus far been lacking in the healthcare sector. However, promising ideas have recently emerged, including adapting existing hospital quality assurance and improvement frameworks to monitor AI-based algorithms’ performance and the data they are trained on and update the algorithms accordingly (Feng et al., 2022). This will likely require the development of multidisciplinary teams within hospitals consisting of clinicians, IT professionals, and biostatisticians who closely collaborate with model developers and regulators (Feng et al., 2022).

While the obstacles discussed in previous sections might slow down the adoption of AI in radiology somewhat, the fear of AI potentially replacing radiologists is unlikely to be one of them. A recent survey from Europe showed that most radiologists did not perceive a reduction in their clinical workload after adopting AI-based solutions (European Society of Radiology (ESR), 2022), likely because, at the same time, demand for radiologists’ services has been continuously rising. Studies from around the world have shown that radiology professionals, particularly those with AI exposure and experience, are generally optimistic about the role of AI in their practice (Y. Chen et al., 2021; Huisman et al., 2021; Ooi et al., 2021; Santomartino & Yi, 2022; Scott et al., 2021).

Conclusion

AI has shown promise in positively impacting virtually every facet of a radiology department’s work - from scheduling and protocolling patient scans to interpreting images and reaching diagnoses. Promising research on AI-based tools in radiology has not yet been widely translated to adoption in routine practice, however, because of a number of complex, partially intertwined issues. Potential solutions exist for many of these challenges, but many of these solutions require further refinement and testing. In the meantime, guidelines are emerging to help potential users of AI-based solutions in radiology navigate the increasing number of commercial products. This encourages their adoption in real-world scenarios, thus allowing their true potential to be uncovered, as well as their weaknesses to be identified and addressed in a safe and effective way. As these incremental improvements are made, these tools will likely evolve to handle more varied data, become integrated into consolidated workflows, become more transparent, and ultimately more useful for increasing efficiency and improving patient care.

References

AAMC Report Reinforces Mounting Physician Shortage. (2021). AAMC. https://www.aamc.org/news-insights/press- releases/aamc-report-reinforces-mounting-physician-shortage

A buyer’s guide to AI in health and care. (2020). NHS Transformation Directorate. https://www.nhsx.nhs.uk/ai-lab/ explore-all-resources/adopt-ai/a-buyers-guide-to-ai-in-health- and-care/

Advanced AI solutions for radiology. (n.d.). Calantic Website. Retrieved July 3, 2022, from https://aivisions.calantic.com/

Aggarwal, R., Farag, S., Martin, G., Ashrafian, H., & Darzi, A. (2021). Patient Perceptions on Data Sharing and Applying Artificial Intelligence to Health Care Data: Cross-sectional Survey. Journal of Medical Internet Research, 23(8), e26162. https://doi.org/10.2196/26162

Aggarwal, R., Sounderajah, V., Martin, G., Ting, D. S. W., Karthikesalingam, A., King, D., Ashrafian, H., & Darzi, A. (2021). Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis. NPJ Digital Medicine, 4(1), 65. https://doi.org/10.1038/s41746-021-00438-z

Agnes, S. A., Anitha, J., Pandian, S. I. A., & Peter, J. D. (2019). Classification of Mammogram Images Using Multiscale all Convolutional Neural Network (MA-CNN). Journal of Medical Systems, 44(1), 30. https://doi.org/10.1007/s10916-019-1494-z

AIF360: A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models. (n.d.). Github. Retrieved June 11, 2022, from https:// github.com/Trusted-AI/AIF360

AI for radiology. (n.d.). Retrieved June 26, 2022, from https://grand-challenge.org/
aiforradiology/?subspeciality=Abdomen&modality=All&ce_ under=All&ce_class=All&fda_class=All&sort_by=last %20 modified&search=

Akagi, M., Nakamura, Y., Higaki, T., Narita, K., Honda, Y., Zhou, J., Yu, Z., Akino, N., & Awai, K. (2019). Deep learning reconstruction improves image quality of abdominal ultra- high-resolution CT. European Radiology, 29(11), 6163–6171. https://doi.org/10.1007/s00330-019-06170-3

Akkus, Z., Cai, J., Boonrod, A., Zeinoddini, A., Weston, A. D., Philbrick, K. A., & Erickson, B. J. (2019). A Survey of Deep- Learning Applications in Ultrasound: Artificial Intelligence- Powered Ultrasound for Improving Clinical Workflow. Journal of the American College of Radiology: JACR, 16(9 Pt B), 1318–1328. https://doi.org/10.1016/j.jacr.2019.06.004

Al-Antari, M. A., Al-Masni, M. A., & Kim, T.-S. (2020). Deep Learning Computer-Aided Diagnosis for Breast Lesion in Digital Mammogram. Advances in Experimental Medicine and Biology, 1213, 59–72. https://doi.org/10.1007/978-3-030-33128-3_4

Alexander, A., Jiang, A., Ferreira, C., & Zurkiya, D. (2020). An Intelligent Future for Medical Imaging: A Market Outlook on Artificial Intelligence for Medical Imaging. Journal of the American College of Radiology: JACR, 17(1 Pt B), 165–170. https:// doi.org/10.1016/j.jacr.2019.07.019

Allen, B., Agarwal, S., Coombs, L., Wald, C., & Dreyer, K. (2021). 2020 ACR Data Science Institute Artificial Intelligence Journal of the American College of Radiology: JACR

Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021). Review of deep learning: concepts, CNN architectures, challenges, applications, future directions.

Arbabshirani, M. R., Fornwalt, B. K., Mongelluzzo, G. J., Suever, J. D., Geise, B. D., Patel, A. A., & Moore, G. J. (2018). Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration. NPJ Digital Medicine, 1, 9. https://doi.org/10.1038/s41746-017-0015-z

Ardila, D., Kiraly, A. P., Bharadwaj, S., Choi, B., Reicher, J. J., Peng, L., Tse, D., Etemadi, M., Ye, W., Corrado, G., Naidich, D. P., & Shetty, S. (2019). End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nature Medicine, 25(6), 954–961. https://doi. org/10.1038/s41591-019-0447-x

Arefan, D., Mohamed, A. A., Berg, W. A., Zuley, M. L., Sumkin, J. H., & Wu, S. (2020). Deep learning modeling using normal mammograms for predicting breast cancer risk. Medical Physics, 47(1), 110–118. https://doi.org/10.1002/mp.13886

Assess-AI. (n.d.). Retrieved July 2, 2022, from https://www. acrdsi.org/DSI-Services/Assess-AI

Azer, S. A. (2019). Deep learning with convolutional neural networks for identification of liver masses and hepatocellular carcinoma: A systematic review. World Journal of Gastrointestinal Oncology, 11(12), 1218–1230. https://doi.org/10.4251/wjgo.v11. i12.1218

Bacchi, S., Zerner, T., Oakden-Rayner, L., Kleinig, T., Patel, S., & Jannes, J. (2020). Deep Learning in the Prediction of Ischaemic Stroke Thrombolysis Functional Outcomes: A Pilot Study. Academic Radiology, 27(2), e19–e23. https://doi. org/10.1016/j.acra.2019.03.015

Baccianella, S., & Gough, T. (n.d.). Why cloud computing is the best option for hospitals adopting AI. Retrieved June 11, 2022, from https://www.aidence.com/articles/cloud-best-option- imaging-ai/

Balint, B. J., Steenburg, S. D., Lin, H., Shen, C., Steele, J. L., & Gunderman, R. B. (2014). Do telephone call interruptions have an impact on radiology resident diagnostic accuracy? Academic Radiology, 21(12), 1623–1628. https://doi.org/10.1016/j. acra.2014.08.001

Baltruschat, I., Steinmeister, L., Nickisch, H., Saalbach, A., Grass, M., Adam, G., Knopp, T., & Ittrich, H. (2021). Smart chest X-ray worklist prioritization using artificial intelligence: a clinical workflow simulation. European Radiology, 31(6), 3837– 3845. https://doi.org/10.1007/s00330-020-07480-7

Bash, S., Johnson, B., Gibbs, W., Zhang, T., Shankaranarayanan, A., & Tanenbaum, L. N. (2021). Deep Learning Image Processing Enables 40 % Faster Spinal MR Scans Which Match or Exceed Quality of Standard of Care : A Prospective Multicenter Multireader Study. Clinical Neuroradiology. https://doi.org/10.1007/s00062-021-01121-2

Bash, S., Wang, L., Airriess, C., Zaharchuk, G., Gong, E., Shankaranarayanan, A., & Tanenbaum, L. N. (2021). Deep Learning Enables 60 % Accelerated Volumetric Brain MRI While Preserving Quantitative Performance: A Prospective, Multicenter, Multireader Trial. AJNR. American Journal of Neuroradiology, 42(12), 2130–2137. https://doi.org/10.3174/ajnr.A7358

Boas, F. E., & Fleischmann, D. (2012). CT artifacts: causes and reduction techniques. Imaging in Medicine, 4(2), 229–240. https://doi.org/10.2217/iim.12.13

Boland, G. W., Duszak, R., Jr, & Kalra, M. (2014). Protocol design and optimization. Journal of the American College of Radiology: JACR, 11(5), 440–441. https://doi.org/10.1016/j. jacr.2014.01.021

Bonavita, I., Rafael-Palou, X., Ceresa, M., Piella, G., Ribas, V., & González Ballester, M. A. (2020). Integration of convolutional neural networks for pulmonary nodule malignancy assessment in a lung cancer classification pipeline. Computer Methods and Programs in Biomedicine, 185, 105172. https://doi.org/10.1016/j.cmpb.2019.105172

Bozkurt, S., Cahan, E. M., Seneviratne, M. G., Sun, R., Lossio- Ventura, J. A., Ioannidis, J. P. A., & Hernandez-Boussard, T. (2020). Reporting of demographic data and representativeness in machine learning models using electronic health records.
Journal of the American Medical Informatics Association: JAMIA, 27(12), 1878–1884. https://doi.org/10.1093/jamia/ocaa164

Brown, A. D., & Marotta, T. R. (2018). Using machine learning for sequence-level automated MRI protocol selection in neuroradiology. Journal of the American Medical Informatics Association: JAMIA, 25(5), 568–571. https://doi.org/10.1093/ jamia/ocx125

Bruls, R. J. M., & Kwee, R. M. (2020). Workload for radiologists during on-call hours: dramatic increase in the past 15 years.
Insights into Imaging, 11(1), 121. https://doi.org/10.1186/ s13244-020-00925-z

Buruk, B., Ekmekci, P. E., & Arda, B. (2020). A critical perspective on guidelines for responsible and trustworthy artificial intelligence. Medicine, Health Care, and Philosophy, 23(3), 387–399. https://doi.org/10.1007/s11019-020-09948-1

Cadario, R., Longoni, C., & Morewedge, C. K. (2021). Understanding, explaining, and utilizing medical artificial intelligence. Nature Human Behaviour, 5(12), 1636–1642. https://doi.org/10.1038/s41562-021-01146-0

Center for Devices, & Radiological Health. (n.d.). Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices. U.S. Food and Drug Administration; FDA. Retrieved July 2, 2022, from https://www.fda.gov/medical-devices/software- medical-device-samd/artificial-intelligence-and-machine- learning-aiml-enabled-medical-devices

Char, D. S., Shah, N. H., & Magnus, D. (2018). Implementing Machine Learning in Health Care - Addressing Ethical Challenges. The New England Journal of Medicine, 378(11), 981–983. https://doi.org/10.1056/NEJMp1714229

Chen, H., Zhang, Y., Kalra, M. K., Lin, F., Chen, Y., Liao, P., Zhou, J., & Wang, G. (2017). Low-Dose CT With a Residual Encoder-Decoder Convolutional Neural Network. IEEE Transactions on Medical Imaging, 36(12), 2524–2535. https://doi.org/10.1109/TMI.2017.2715284

Chen, Y., Stavropoulou, C., Narasinkan, R., Baker, A., & Scarbrough, H. (2021). Professionals’ responses to the introduction of AI innovations in radiology and their implications for future adoption: a qualitative study. BMC Health Services Research, 21(1), 813. https://doi.org/10.1186/ s12913-021-06861-y

Choe, J., Lee, S. M., Do, K.-H., Lee, G., Lee, J.-G., Lee, S. M., & Seo, J. B. (2019). Deep Learning-based Image Conversion of CT Reconstruction Kernels Improves Radiomics Reproducibility for Pulmonary Nodules or Masses. Radiology, 292(2), 365–373. https://doi.org/10.1148/radiol.2019181960

Choi, K. S., Choi, S. H., & Jeong, B. (2019). Prediction of IDH genotype in gliomas with dynamic susceptibility contrast perfusion MR imaging using an explainable recurrent neural network. Neuro-Oncology, 21(9), 1197–1209. https://doi.org/10.1093/neuonc/noz095

Chong, L. R., Tsai, K. T., Lee, L. L., Foo, S. G., & Chang, P. C. (2020). Artificial Intelligence Predictive Analytics in the Management of Outpatient MRI Appointment No-Shows. AJR. American Journal of Roentgenology, 215(5), 1155–1162. https://doi.org/10.2214/AJR.19.22594

Cikes, M., Sanchez-Martinez, S., Claggett, B., Duchateau, N., Piella, G., Butakoff, C., Pouleur, A. C., Knappe, D., Biering- Sørensen, T., Kutyifa, V., Moss, A., Stein, K., Solomon, S. D., & Bijnens, B. (2019). Machine learning-based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy. European Journal of Heart Failure, 21(1), 74–85. https://doi.org/10.1002/ejhf.1333

Ciompi, F., Chung, K., van Riel, S. J., Setio, A. A. A., Gerke, P. K., Jacobs, C., Scholten, E. T., Schaefer-Prokop, C., Wille,
M. M. W., Marchianò, A., Pastorino, U., Prokop, M., & van Ginneken, B. (2017). Towards automatic pulmonary nodule management in lung cancer screening with deep learning. Scientific Reports, 7, 46479. https://doi.org/10.1038/srep46479

Clinical radiology UK workforce census 2019 report. (2019). https://www.rcr.ac.uk/publication/clinical-radiology-uk- workforce-census-2019-report

Cloud security for healthcare services. (2021, January 14). ENISA. https://www.enisa.europa.eu/publications/cloud- security-for-healthcare-services/

CONSORT-AI and SPIRIT-AI Steering Group. (2019). Reporting guidelines for clinical trials evaluating artificial intelligence interventions are needed. Nature Medicine, 25(10), 1467–1468. https://doi.org/10.1038/s41591-019-0603-3

Curated marketplace. (2018, May 22). Blackford. https://www.blackfordanalysis.com/applications/

Dance, A. (2021). AI spots cell structures that humans can’t. Nature. 592 (7852), 154–155.

Dantas, L. F., Fleck, J. L., Cyrino Oliveira, F. L., & Hamacher, S. (2018). No-shows in appointment scheduling - a systematic literature review. Health Policy, 122(4), 412–421. https://doi.org/10.1016/j.healthpol.2018.02.002

Deák, Z., Grimm, J. M., Treitl, M., Geyer, L. L., Linsenmaier, U., Körner, M., Reiser, M. F., & Wirth, S. (2013). Filtered back projection, adaptive statistical iterative reconstruction, and a model-based iterative reconstruction in abdominal CT: an experimental clinical study. Radiology, 266(1), 197–206. https://doi.org/10.1148/radiol.12112707

Dembrower, K., Liu, Y., Azizpour, H., Eklund, M., Smith, K., Lindholm, P., & Strand, F. (2020). Comparison of a Deep
Learning Risk Score and Standard Mammographic Density Score for Breast Cancer Risk Prediction. Radiology, 294(2), 265–272. https://doi.org/10.1148/radiol.2019190872

Do, B. H., Langlotz, C., & Beaulieu, C. F. (2017). Bone Tumor Diagnosis Using a Naïve Bayesian Model of Demographic and Radiographic Features. Journal of Digital Imaging, 30(5), 640–647. https://doi.org/10.1007/s10278-017-0001-7

Dou, Q., Yu, L., Chen, H., Jin, Y., Yang, X., Qin, J., & Heng, P.-A. (2017). 3D deeply supervised network for automated segmentation of volumetric medical images. Medical Image Analysis, 41, 40–54. https://doi.org/10.1016/j. media.2017.05.001

Eche, T., Schwartz, L. H., Mokrane, F.-Z., & Dercle, L. (2021). Toward Generalizability in the Deployment of Artificial Intelligence in Radiology: Role of Computation Stress Testing to Overcome Underspecification. Radiology. Artificial Intelligence, 3(6), e210097. https://doi.org/10.1148/ryai.2021210097

England, N. H. S., & Improvement, N. H. S. (2019). NHS diagnostic waiting times and activity data. NHS. https://www. england.nhs.uk/statistics/wp-content/uploads/sites/2/2021/12/ DWTA-Report-October-2021_M43D4.pdf

Esmaeilzadeh, P. (2020). Use of AI-based tools for healthcare purposes: a survey study from consumers’ perspectives. BMC Medical Informatics and Decision Making, 20(1), 170. https://doi. org/10.1186/s12911-020-01191-1

Esses, S. J., Lu, X., Zhao, T., Shanbhogue, K., Dane, B., Bruno, M., & Chandarana, H. (2018). Automated image quality evaluation of T2 -weighted liver MRI utilizing deep learning architecture. Journal of Magnetic Resonance Imaging: JMRI, 47(3), 723–728. https://doi.org/10.1002/jmri.25779

European Society of Radiology (ESR). (2022). Current practical experience with artificial intelligence in clinical radiology:
a survey of the European Society of Radiology. Insights into Imaging, 13(1), 107. https://doi.org/10.1186/s13244-022- 01247-y

Faron, A., Sichtermann, T., Teichert, N., Luetkens, J. A., Keulers, A., Nikoubashman, O., Freiherr, J., Mpotsaris, A., & Wiesmann, M. (2020). Performance of a Deep-Learning Neural Network to Detect Intracranial Aneurysms from 3D TOF-MRA Compared to Human Readers. Clinical Neuroradiology, 30(3), 591–598. https://doi.org/10.1007/s00062-019-00809-w

Feng, J., Phillips, R. V., Malenica, I., Bishara, A., Hubbard, A. E., Celi, L. A., & Pirracchio, R. (2022). Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare. NPJ Digital Medicine, 5(1), 66. https://doi.org/10.1038/s41746-022- 00611-y

Finlayson, S. G., Chung, H. W., Kohane, I. S., & Beam, A. L. (2018). Adversarial Attacks Against Medical Deep Learning Systems. In arXiv [cs.CR]. arXiv. https://doi.org/10.1145/nnnnnnn. nnnnnnn

Flanders, A. E., Prevedello, L. M., Shih, G., Halabi, S. S., Kalpathy-Cramer, J., Ball, R., Mongan, J. T., Stein, A., Kitamura, F. C., Lungren, M. P., Choudhary, G., Cala, L., Coelho, L., Mogensen, M., Morón, F., Miller, E., Ikuta, I., Zohrabian, V., McDonnell, O., … RSNA-ASNR 2019 Brain Hemorrhage CT Annotators. (2020). Construction of a Machine Learning Dataset through Collaboration: The RSNA 2019 Brain CT Hemorrhage Challenge. Radiology. Artificial Intelligence, 2(3), e190211. https://doi.org/10.1148/ryai.2020190211

Freeman, K., Geppert, J., Stinton, C., Todkill, D., Johnson, S., Clarke, A., & Taylor-Phillips, S. (2021). Use of artificial intelligence for image analysis in breast cancer screening programmes: systematic review of test accuracy. BMJ , 374, n1872. https://doi.org/10.1136/bmj.n1872

General Data Protection Regulation (GDPR) – Official Legal Text. (2016, July 13). General Data Protection Regulation (GDPR). https://gdpr-info.eu/

Ghafur, S., Van Dael, J., Leis, M., Darzi, A., & Sheikh, A. (2020). Public perceptions on data sharing: key insights from the UK and the USA. The Lancet. Digital Health, 2(9), e444–e446. https://doi.org/10.1016/S2589-7500(20)30161-8

Ghani, M. U., & Clem Karl, W. (2019). Fast Enhanced CT Metal Artifact Reduction using Data Domain Deep Learning. In arXiv [cs.CV]. arXiv. http://arxiv.org/abs/1904.04691

Ghassemi, M., Oakden-Rayner, L., & Beam, A. L. (2021). The false hope of current approaches to explainable artificial intelligence in health care. The Lancet. Digital Health, 3(11), e745–e750. https://doi.org/10.1016/S2589-7500(21)00208-9

Ginat, D. T. (2020). Analysis of head CT scans flagged by deep learning software for acute intracranial hemorrhage. Neuroradiology, 62(3), 335–340. https://doi.org/10.1007/ s00234-019-02330-w

Habli, I., Lawton, T., & Porter, Z. (2020). Artificial intelligence in health care: accountability and safety. Bulletin of the World Health Organization, 98(4), 251–256. https://doi.org/10.2471/ BLT.19.237487

Halabi, S. S., Prevedello, L. M., Kalpathy-Cramer, J., Mamonov, A. B., Bilbily, A., Cicero, M., Pan, I., Pereira, L. A., Sousa, R. T., Abdala, N., Kitamura, F. C., Thodberg, H. H., Chen, L., Shih, G., Andriole, K., Kohli, M. D., Erickson, B. J., & Flanders, A. E. (2019). The RSNA Pediatric Bone Age Machine Learning Challenge. Radiology, 290(2), 498–503. https://doi. org/10.1148/radiol.2018180736

Hargreaves, B. A., Worters, P. W., Pauly, K. B., Pauly, J. M., Koch, K. M., & Gold, G. E. (2011). Metal-induced artifacts in MRI. AJR. American Journal of Roentgenology, 197(3), 547–555. https://doi.org/10.2214/AJR.11.7364

Harry, E., Sinsky, C., Dyrbye, L. N., Makowski, M. S., Trockel, M., Tutty, M., Carlasare, L. E., West, C. P., & Shanafelt, T. D. (2021). Physician Task Load and the Risk of Burnout Among US Physicians in a National Survey. Joint Commission Journal on Quality and Patient Safety / Joint Commission Resources, 47(2), 76–85. https://doi.org/10.1016/j.jcjq.2020.09.011

Hata, A., Yanagawa, M., Yamagata, K., Suzuki, Y., Kido, S., Kawata, A., Doi, S., Yoshida, Y., Miyata, T., Tsubamoto, M., Kikuchi, N., & Tomiyama, N. (2021). Deep learning algorithm for detection of aortic dissection on non-contrast-enhanced CT. European Radiology, 31(2), 1151–1159. https://doi.org/10.1007/ s00330-020-07213-w

Hauptmann, A., Arridge, S., Lucka, F., Muthurangu, V., & Steeden, J. A. (2019). Real-time cardiovascular MR with
spatio-temporal artifact suppression using deep learning-proof of concept in congenital heart disease. Magnetic Resonance in Medicine: Official Journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine, 81(2), 1143–1156. https://doi.org/10.1002/mrm.27480

Health Ethics & Governance. (2021, June 28). Ethics and governance of artificial intelligence for health. World Health Organization. https://www.who.int/publications/i/ item/9789240029200

He, L., Li, H., Dudley, J. A., Maloney, T. C., Brady, S. L., Somasundaram, E., Trout, A. T., & Dillman, J. R. (2019). Machine Learning Prediction of Liver Stiffness Using Clinical and T2-Weighted MRI Radiomic Data. AJR. American Journal of Roentgenology, 213(3), 592–601. https://doi.org/10.2214/ AJR.19.21082

Herent, P., Schmauch, B., Jehanno, P., Dehaene, O., Saillard, C., Balleyguier, C., Arfi-Rouche, J., & Jégou, S. (2019).
Detection and characterization of MRI breast lesions using deep learning. Diagnostic and Interventional Imaging, 100(4), 219–225. https://doi.org/10.1016/j.diii.2019.02.008

Hinton, B., Ma, L., Mahmoudzadeh, A. P., Malkov, S., Fan, B., Greenwood, H., Joe, B., Lee, V., Kerlikowske, K., & Shepherd,
J. (2019). Deep learning networks find unique mammographic differences in previous negative mammograms between interval and screen-detected cancers: a case-case study. Cancer Imaging: The Official Publication of the International Cancer Imaging Society, 19(1), 41. https://doi.org/10.1186/s40644-019-0227-3

Holzinger, A., Biemann, C., Pattichis, C. S., & Kell, D. B. (2017). What do we need to build explainable AI systems for the medical domain? In arXiv [cs.AI]. arXiv. http://arxiv.org/ abs/1712.09923

Hötker, A. M., Da Mutten, R., Tiessen, A., Konukoglu, E., & Donati, O. F. (2021). Improving workflow in prostate MRI:
AI-based decision-making on biparametric or multiparametric MRI. Insights into Imaging, 12(1), 112. https://doi.org/10.1186/ s13244-021-01058-7

Huang, S.-C., Kothari, T., Banerjee, I., Chute, C., Ball, R. L., Borus, N., Huang, A., Patel, B. N., Rajpurkar, P., Irvin, J., Dunnmon, J., Bledsoe, J., Shpanskaya, K., Dhaliwal, A., Zamanian, R., Ng, A. Y., & Lungren, M. P. (2020). PENet-a scalable deep-learning model for automated diagnosis of pulmonary embolism using volumetric CT imaging. NPJ Digital Medicine, 3, 61. https://doi.org/10.1038/s41746-020-0266-y

Huang, S.-C., Pareek, A., Seyyedi, S., Banerjee, I., & Lungren, M. P. (2020). Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines. NPJ Digital Medicine, 3, 136. https:// doi.org/10.1038/s41746-020-00341-z

Huisman, M., Ranschaert, E., Parker, W., Mastrodicasa, D., Koci, M., Pinto de Santos, D., Coppola, F., Morozov, S., Zins, M., Bohyn, C., Koç, U., Wu, J., Veean, S., Fleischmann, D., Leiner, T., & Willemink, M. J. (2021). An international survey on AI in radiology in 1,041 radiologists and radiology residents part 1: fear of replacement, knowledge, and attitude. European Radiology, 31(9), 7058–7066. https://doi.org/10.1007/s00330-021-07781-5

Hu, S.-Y., Santus, E., Forsyth, A. W., Malhotra, D., Haimson, J., Chatterjee, N. A., Kramer, D. B., Barzilay, R., Tulsky,
J. A., & Lindvall, C. (2019). Can machine learning improve patient selection for cardiac resynchronization therapy? PloS One, 14(10), e0222397. https://doi.org/10.1371/journal. pone.0222397

Hwang, E. J., Nam, J. G., Lim, W. H., Park, S. J., Jeong, Y. S., Kang, J. H., Hong, E. K., Kim, T. M., Goo, J. M., Park, S., Kim, K. H., & Park, C. M. (2019). Deep Learning for Chest Radiograph Diagnosis in the Emergency Department. Radiology, 293(3), 573–580. https://doi.org/10.1148/radiol.2019191225

Hwang, E. J., Park, S., Jin, K.-N., Kim, J. I., Choi, S. Y., Lee, J. H., Goo, J. M., Aum, J., Yim, J.-J., Park, C. M., & Deep Learning- Based Automatic Detection Algorithm Development and Evaluation Group. (2019). Development and Validation of a Deep Learning-based Automatic Detection Algorithm for Active Pulmonary Tuberculosis on Chest Radiographs. Clinical Infectious Diseases: An Official Publication of the Infectious Diseases Society of America, 69(5), 739–747. https://doi.org/10.1093/cid/ciy967

Hwang, S., Kim, H.-E., Jeong, J., & Kim, H.-J. (2016). A novel approach for tuberculosis screening based on deep convolutional neural networks. In G. D. Tourassi & S. G. Armato (Eds.), Medical Imaging 2016: Computer-Aided Diagnosis. SPIE. https://doi.org/10.1117/12.2216198

IBM Watson Studio - Model Risk Management. (n.d.). Retrieved June 11, 2022, from https://www.ibm.com/cloud/ watson-studio/model-risk-management

Imaging AI Marketplace - overview. (n.d.). Retrieved June 11, 2022, from https://www.ibm.com/products/imaging-ai- marketplace

Jamaludin, A., Lootus, M., Kadir, T., Zisserman, A., Urban, J., Battié, M. C., Fairbank, J., McCall, I., & Genodisc Consortium. (2017). ISSLS PRIZE IN BIOENGINEERING SCIENCE 2017:
Automation of reading of radiological features from magnetic resonance images (MRIs) of the lumbar spine without human intervention is comparable with an expert radiologist. European Spine Journal: Official Publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society, 26(5), 1374–1383. https:// doi.org/10.1007/s00586-017-4956-3

Kaissis, G. A., Makowski, M. R., Rückert, D., & Braren, R. F. (2020). Secure, privacy-preserving and federated machine learning in medical imaging. Nature Machine Intelligence, 2(6), 305–311. https://doi.org/10.1038/s42256-020-0186-1

Kaissis, G., Ziller, A., Passerat-Palmbach, J., Ryffel, T., Usynin, D., Trask, A., Lima, I., Mancuso, J., Jungmann, F., Steinborn, M.-M., Saleh, A., Makowski, M., Rueckert, D., & Braren, R. (2021). End-to-end privacy preserving deep learning on multi-institutional medical imaging. Nature Machine Intelligence, 3(6), 473–484. https://doi.org/10.1038/s42256-021-00337-8

Kalra, A., Chakraborty, A., Fine, B., & Reicher, J. (2020). Machine Learning for Automation of Radiology Protocols for Quality and Efficiency Improvement. Journal of the American College of Radiology: JACR, 17(9), 1149–1158. https://doi.org/10.1016/j.jacr.2020.03.012

Kao, P.-Y., Chen, J. W., & Manjunath, B. S. (2019). Improving 3D U-Net for Brain Tumor Segmentation by Utilizing Lesion Prior. In arXiv [cs.CV]. arXiv. http://arxiv.org/abs/1907.00281

Kapoor, N., Lacson, R., & Khorasani, R. (2020). Workflow Applications of Artificial Intelligence in Radiology and an Overview of Available Tools. Journal of the American College of Radiology: JACR, 17(11), 1363–1370. https://doi.org/10.1016/j. jacr.2020.08.016

Kathirvelu, D., Vinupritha, P., & Kalpana, V. (2019). A computer aided diagnosis system for measurement of mandibular cortical thickness on dental panoramic radiographs in prediction of women with low bone mineral density. Journal of Medical Systems, 43(6), 148. https://doi.org/10.1007/s10916-019-1268-7

Ker, J., Singh, S. P., Bai, Y., Rao, J., Lim, T., & Wang, L. (2019). Image Thresholding Improves 3-Dimensional Convolutional Neural Network Diagnosis of Different Acute Brain Hemorrhages on Computed Tomography Scans. Sensors, 19(9). https://doi.org/10.3390/s19092167

Khan, F. A., Majidulla, A., Tavaziva, G., Nazish, A., Abidi, S. K., Benedetti, A., Menzies, D., Johnston, J. C., Khan, A. J., & Saeed, S. (2020). Chest x-ray analysis with deep learning- based software as a triage test for pulmonary tuberculosis: a prospective study of diagnostic accuracy for culture-confirmed disease. The Lancet. Digital Health, 2(11), e573–e581. https://doi.org/10.1016/S2589-7500(20)30221-1

Kim, D. W., Jang, H. Y., Kim, K. W., Shin, Y., & Park, S. H. (2019). Design Characteristics of Studies Reporting the Performance of Artificial Intelligence Algorithms for Diagnostic Analysis of Medical Images: Results from Recently Published Papers. Korean Journal of Radiology: Official Journal of the Korean Radiological Society, 20(3), 405–410. https://doi.org/10.3348/ kjr.2019.0025

Kim, K. H., & Park, S.-H. (2017). Artificial neural network for suppression of banding artifacts in balanced steady-state free precession MRI. Magnetic Resonance Imaging, 37, 139–146. https://doi.org/10.1016/j.mri.2016.11.020

Korteling, J. E. H., van de Boer-Visschedijk, G. C., Blankendaal, R. A. M., Boonekamp, R. C., & Eikelboom, A. R. (2021). Human- versus Artificial Intelligence. Frontiers in Artificial Intelligence 4, 622364. https://doi.org/10.3389/ frai.2021.622364

Kühl, N., Goutier, M., Baier, L., Wolff, C., & Martin, D. (2020). Human vs. supervised machine learning: Who learns patterns faster? In arXiv [cs.AI] arXiv. http://arxiv.org/abs/2012.03661

Kuo, W., Häne, C., Mukherjee, P., Malik, J., & Yuh, E. L. (2019). Expert-level detection of acute intracranial hemorrhage on head computed tomography using deep learning. Proceedings of the National Academy of Sciences of the United States of America, 116(45), 22737–22745. https://doi.org/10.1073/ pnas.1908021116

Langerhuizen, D. W. G., Janssen, S. J., Mallee, W. H., van den Bekerom, M. P. J., Ring, D., Kerkhoffs, G. M. M. J., Jaarsma,
R. L., & Doornberg, J. N. (2019). What Are the Applications and Limitations of Artificial Intelligence for Fracture Detection and Classification in Orthopaedic Trauma Imaging? A Systematic Review. Clinical Orthopaedics and Related Research, 477(11), 2482–2491. https://doi.org/10.1097/CORR.0000000000000848

Lang, N., Zhang, Y., Zhang, E., Zhang, J., Chow, D., Chang, P., Yu, H. J., Yuan, H., & Su, M.-Y. (2019). Differentiation of spinal metastases originated from lung and other cancers using radiomics and deep learning based on DCE-MRI. Magnetic Resonance Imaging, 64, 4–12. https://doi.org/10.1016/j. mri.2019.02.013

Larrazabal, A. J., Nieto, N., Peterson, V., Milone, D. H., & Ferrante, E. (2020). Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis. Proceedings of the National Academy of Sciences of the United States of America, 117(23), 12592–12594. https://doi.org/10.1073/pnas.1919012117

Lee, J.-S., Adhikari, S., Liu, L., Jeong, H.-G., Kim, H., & Yoon, S.-J. (2019). Osteoporosis detection in panoramic radiographs using a deep convolutional neural network-based computer- assisted diagnosis system: a preliminary study. Dento Maxillo Facial Radiology, 48(1), 20170344. https://doi.org/10.1259/ dmfr.20170344

Lee, Y. H. (2018). Efficiency Improvement in a Busy Radiology Practice: Determination of Musculoskeletal Magnetic Resonance Imaging Protocol Using Deep-Learning Convolutional Neural Networks. Journal of Digital Imaging, 31(5), 604–610. https://doi.org/10.1007/s10278-018-0066-y

Leiner, T., Bennink, E., Mol, C. P., Kuijf, H. J., & Veldhuis, W. B. (2021). Bringing AI to the clinic: blueprint for a vendor-neutral AI deployment infrastructure. Insights into Imaging, 12(1), 11. https://doi.org/10.1186/s13244-020-00931-1

Lekadir, K., Osuala, R., Gallin, C., Lazrak, N., Kushibar, K., Tsakou, G., Aussó, S., Alberich, L. C., Marias, K., Tsiknakis, M., Colantonio, S., Papanikolaou, N., Salahuddin, Z., Woodruff, H. C., Lambin, P., & Martí-Bonmatí, L. (2021). FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging. In arXiv [cs.CV]. arXiv. http://arxiv.org/ abs/2109.09658

Letourneau-Guillon, L., Camirand, D., Guilbert, F., & Forghani, R. (2020). Artificial Intelligence Applications for Workflow, Process Optimization and Predictive Analytics. Neuroimaging Clinics of North America, 30(4), e1–e15. https://doi.org/10.1016/j.nic.2020.08.008

Levin, D. C., Parker, L., & Rao, V. M. (2017). Recent Trends in Imaging Use in Hospital Settings: Implications for Future Planning. Journal of the American College of Radiology: JACR, 14(3), 331–336. https://doi.org/10.1016/j.jacr.2016.08.025

Lindsey, R., Daluiski, A., Chopra, S., Lachapelle, A., Mozer, M., Sicular, S., Hanel, D., Gardner, M., Gupta, A., Hotchkiss, R., & Potter, H. (2018). Deep neural network improves fracture detection by clinicians. Proceedings of the National Academy of Sciences of the United States of America, 115(45), 11591–11596. https://doi.org/10.1073/pnas.1806905115

Liu, F., Tang, J., Ma, J., Wang, C., Ha, Q., Yu, Y., & Zhou, Z. (2021). The application of artificial intelligence to chest medical image analysis. Intelligent Medicine, 1(3), 104–117. https://doi.org/10.1016/j.imed.2021.06.004

Liu, F., Zhou, Z., Samsonov, A., Blankenbaker, D., Larison, W., Kanarek, A., Lian, K., Kambhampati, S., & Kijowski, R. (2018). Deep Learning Approach for Evaluating Knee MR Images: Achieving High Diagnostic Performance for Cartilage Lesion Detection. Radiology, 289(1), 160–169. https://doi.org/10.1148/ radiol.2018172986

Liu, X., Cruz Rivera, S., Moher, D., Calvert, M. J., Denniston, A. K., & SPIRIT-AI and CONSORT-AI Working Group. (2020). Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nature Medicine, 26(9), 1364–1374. https://doi.org/10.1038/ s41591-020-1034-x

Liu, X., Faes, L., Kale, A. U., Wagner, S. K., Fu, D. J., Bruynseels, A., Mahendiran, T., Moraes, G., Shamdas, M., Kern, C., Ledsam, J. R., Schmid, M. K., Balaskas, K., Topol, E. J., Bachmann, L. M., Keane, P. A., & Denniston, A. K. (2019). A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. The Lancet. Digital Health, 1(6), e271–e297. https://doi.org/10.1016/S2589- 7500(19)30123-2

Li, X., Shen, L., Xie, X., Huang, S., Xie, Z., Hong, X., & Yu, J. (2020). Multi-resolution convolutional networks for chest X-ray radiograph based lung nodule detection. Artificial Intelligence in Medicine, 103, 101744. https://doi.org/10.1016/j. artmed.2019.101744

Lotan, E., Tschider, C., Sodickson, D. K., Caplan, A. L., Bruno, M., Zhang, B., & Lui, Y. W. (2020). Medical Imaging and Privacy in the Era of Artificial Intelligence: Myth, Fallacy, and the Future. Journal of the American College of Radiology: JACR, 17(9), 1159–1162. https://doi.org/10.1016/j.jacr.2020.04.007

Maegerlein, C., Fischer, J., Mönch, S., Berndt, M., Wunderlich, S., Seifert, C. L., Lehm, M., Boeckh-Behrens, T., Zimmer, C., & Friedrich, B. (2019). Automated Calculation of the Alberta Stroke Program Early CT Score: Feasibility and
Reliability. Radiology, 291(1), 141–148. https://doi.org/10.1148/ radiol.2019181228

Mairhöfer, D., Laufer, M., Simon, P. M., Sieren, M., Bischof, A., Käster, T., Barth, E., Barkhausen, J., & Martinetz, T. (2021). An AI-based Framework for Diagnostic Quality Assessment of Ankle Radiographs. https://openreview.net/ pdf?id=bj04hJss_xZ

Mancio, J., Pashakhanloo, F., El-Rewaidy, H., Jang, J., Joshi, G., Csecs, I., Ngo, L., Rowin, E., Manning, W., Maron, M., & Nezafat, R. (2022). Machine learning phenotyping of scarred myocardium from cine in hypertrophic cardiomyopathy. European Heart Journal Cardiovascular Imaging, 23(4), 532–542. https://doi.org/10.1093/ehjci/jeab056

Matsoukas, S., Morey, J., Lock, G., Chada, D., Shigematsu, T., Marayati, N. F., Delman, B. N., Doshi, A., Majidi, S.,
De Leacy, R., Kellner, C. P., & Fifi, J. T. (2022). AI software detection of large vessel occlusion stroke on CT angiography: a real-world prospective diagnostic test accuracy study. Journal of Neurointerventional Surgery. https://doi.org/10.1136/ neurintsurg-2021-018391

McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., Back, T., Chesus, M., Corrado, G. S., Darzi, A., Etemadi, M., Garcia-Vicente, F., Gilbert, F. J., Halling-Brown, M., Hassabis, D., Jansen, S., Karthikesalingam, A., Kelly, C. J., King, D., … Shetty, S. (2020). International evaluation of an AI system for breast cancer screening. Nature, 577(7788), 89–94. https://doi.org/10.1038/s41586-019-1799-6

McLeavy, C. M., Chunara, M. H., Gravell, R. J., Rauf, A., Cushnie, A., Staley Talbot, C., & Hawkins, R. M. (2021). The future of CT: deep learning reconstruction. Clinical Radiology, 76(6), 407–415. https://doi.org/10.1016/j.crad.2021.01.010

Medical AI evaluation. (n.d.). Retrieved June 26, 2022, from https://ericwu09.github.io/medical-ai-evaluation/

Mlynarski, P., Delingette, H., Criminisi, A., & Ayache, N. (2019). Deep learning with mixed supervision for brain tumor segmentation. Journal of Medical Imaging (Bellingham, Wash.), 6(3), 034002. https://doi.org/10.1117/1.JMI.6.3.034002

Mongan, J., Moy, L., & Kahn, C. E., Jr. (2020). Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers. Radiology. Artificial Intelligence, 2(2), e200029. https://doi.org/10.1148/ryai.2020200029

Moon, H., Huo, Y., Abramson, R. G., Peters, R. A., Assad, A., Moyo, T. K., Savona, M. R., & Landman, B. A. (2019). Acceleration of spleen segmentation with end-to-end deep learning method and automated pipeline. Computers in Biology and Medicine, 107, 109–117. https://doi.org/10.1016/j.compbiomed.2019.01.018

Murdoch, B. (2021). Privacy and artificial intelligence: challenges for protecting health information in a new era. BMC Medical Ethics, 22(1), 122. https://doi.org/10.1186/s12910-021-00687-3

Nagendran, M., Chen, Y., Lovejoy, C. A., Gordon, A. C., Komorowski, M., Harvey, H., Topol, E. J., Ioannidis, J. P. A., Collins, G. S., & Maruthappu, M. (2020). Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies. BMJ, 368. https://doi.org/10.1136/bmj.m689

Nair, T., Precup, D., Arnold, D. L., & Arbel, T. (2020). Exploring uncertainty measures in deep networks for Multiple sclerosis lesion detection and segmentation. Medical Image Analysis, 59, 101557. https://doi.org/10.1016/j.media.2019.101557

Nakao, T., Hanaoka, S., Nomura, Y., Sato, I., Nemoto, M., Miki, S., Maeda, E., Yoshikawa, T., Hayashi, N., & Abe, O. (2018). Deep neural network-based computer-assisted detection of cerebral aneurysms in MR angiography. Journal of Magnetic Resonance Imaging: JMRI, 47(4), 948–953. https://doi.org/10.1002/jmri.25842

Nam, J. G., Kim, M., Park, J., Hwang, E. J., Lee, J. H., Hong, J. H., Goo, J. M., & Park, C. M. (2021). Development and validation of a deep learning algorithm detecting 10 common abnormalities on chest radiographs. The European Respiratory Journal: Official Journal of the European Society for Clinical Respiratory Physiology, 57(5). https://doi.org/10.1183/13993003.03061-2020

Narayana, P. A., Coronado, I., Sujit, S. J., Wolinsky, J. S., Lublin, F. D., & Gabr, R. E. (2020). Deep Learning for Predicting Enhancing Lesions in Multiple Sclerosis from Noncontrast. MRI. Radiology, 294(2), 398–404. https://doi.org/10.1148/ radiol.2019191061

National Institute for Health and Care Excellence (NICE). (n.d.). Evidence standards framework for digital health technologies. Retrieved June 10, 2022, from https://www.nice.org.uk/corporate/ecd7

Neisius, U., El-Rewaidy, H., Nakamori, S., Rodriguez, J., Manning, W. J., & Nezafat, R. (2019). Radiomic Analysis of Myocardial Native T1 Imaging Discriminates Between Hypertensive Heart Disease and Hypertrophic Cardiomyopathy. JACC. Cardiovascular Imaging, 12(10), 1946–1954. https://doi. org/10.1016/j.jcmg.2018.11.024

Nelson, A., Herron, D., Rees, G., & Nachev, P. (2019). Predicting scheduled hospital attendance with artificial intelligence. Npj Digital Medicine, 2(1), 26. https://doi.org/10.1038/s41746-019-0103-3

Nielsen, A., Hansen, M. B., Tietze, A., & Mouridsen, K. (2018). Prediction of Tissue Outcome and Assessment of Treatment Effect in Acute Ischemic Stroke Using Deep Learning. Stroke; a Journal of Cerebral Circulation, STROKEAHA.117.019740. https://doi.org/10.1161/STROKEAHA.117.019740

Norori, N., Hu, Q., Aellen, F. M., Faraci, F. D., & Tzovara, A. (2021). Addressing bias in big data and AI for health care: A call for open science. Patterns (New York, N.Y.), 2(10), 100347. https://doi.org/10.1016/j.patter.2021.100347

O’Connor, S. D., & Bhalla, M. (2021). Should Artificial Intelligence Tell Radiologists Which Study to Read Next? [Review of Should Artificial Intelligence Tell Radiologists Which Study to Read Next?]. Radiology. Artificial Intelligence, 3(2), e210009. https://doi.org/10.1148/ryai.2021210009

Office for Civil Rights (OCR). (2012, September 7). Guidance Regarding Methods for De-identification of Protected Health Information in Accordance with the Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule. HHS.gov; US Department of Health and Human Services. https://www.hhs.gov/hipaa/for-professionals/privacy/special-topics/de- identification/index.html

Oktay, O., Schlemper, J., Le Folgoc, L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N. Y.,
Kainz, B., Glocker, B., & Rueckert, D. (2018). Attention U-Net: Learning Where to Look for the Pancreas. In arXiv [cs.CV]. arXiv. http://arxiv.org/abs/1804.03999

Olczak, J., Fahlberg, N., Maki, A., Razavian, A. S., Jilert, A., Stark, A., Sköldenberg, O., & Gordon, M. (2017). Artificial intelligence for analyzing orthopedic trauma radiographs. Acta Orthopaedica, 88(6), 581–586. https://doi.org/10.1080/1745367
4.2017.1344459

Omoumi, P., Ducarouge, A., Tournier, A., Harvey, H., Kahn, C. E., Jr, Louvet-de Verchère, F., Pinto Dos Santos, D., Kober, T., & Richiardi, J. (2021). To buy or not to buy-evaluating commercial AI solutions in radiology (the ECLAIR guidelines). European Radiology, 31(6), 3786–3796. https://doi.org/10.1007/ s00330-020-07684-x

O’Neill, T. J., Xi, Y., Stehel, E., Browning, T., Ng, Y. S., Baker, C., & Peshock, R. M. (2021). Active Reprioritization of the Reading Worklist Using Artificial Intelligence Has a Beneficial Effect on the Turnaround Time for Interpretation of Head CT with Intracranial Hemorrhage. Radiology. Artificial Intelligence, 3(2), e200024. https://doi.org/10.1148/ryai.2020200024

Ooi, S. K. G., Makmur, A., Soon, A. Y. Q., Fook-Chong, S., Liew, C., Sia, S. Y., Ting, Y. H., & Lim, C. Y. (2021). Attitudes toward artificial intelligence in radiology with learner needs assessment within radiology residency programmes: a national multi-programme survey. Singapore Medical Journal, 62(3), 126–134. https://doi.org/10.11622/smedj.2019141

Pan, Y., Shi, D., Wang, H., Chen, T., Cui, D., Cheng, X., & Lu, Y. (2020). Automatic opportunistic osteoporosis screening using low-dose chest computed tomography scans obtained for lung cancer screening. European Radiology, 30(7), 4107–4116. https://doi.org/10.1007/s00330-020-06679-y

Park, H. J., Kim, S. M., La Yun, B., Jang, M., Kim, B., Jang, J. Y., Lee, J. Y., & Lee, S. H. (2019). A computer-aided diagnosis system using artificial intelligence for the diagnosis and characterization of breast masses on ultrasound: Added value for the inexperienced breast radiologist. Medicine, 98(3), e14146. https://doi.org/10.1097/MD.0000000000014146

Price, I. I., & Nicholson, W. (2019). Medical AI and Contextual Bias. https://papers.ssrn.com/abstract=3347890

Puvanasunthararajah, S., Fontanarosa, D., Wille, M.-L., & Camps, S. M. (2021). The application of metal artifact reduction methods on computed tomography scans for radiotherapy applications: A literature review. Journal of Applied Clinical Medical Physics / American College of Medical Physics, 22(6), 198–223. https://doi.org/10.1002/acm2.13255

Qin, Z. Z., Sander, M. S., Rai, B., Titahong, C. N., Sudrungrot, S., Laah, S. N., Adhikari, L. M., Carter, E. J., Puri, L., Codlin, A. J., & Creswell, J. (2019). Using artificial intelligence to read chest radiographs for tuberculosis detection: A multi-site evaluation of the diagnostic accuracy of three deep learning systems. Scientific Reports, 9(1), 15000. https://doi.org/10.1038/ s41598-019-51503-3

Ramspek, C. L., Jager, K. J., Dekker, F. W., Zoccali, C., & van Diepen, M. (2021). External validation of prognostic models: what, why, how, when and where?

Reddy, S., Allan, S., Coghlan, S., & Cooper, P. (2020). A governance model for the application of AI in health care. Journal of the American Medical Informatics Association: JAMIA, 27(3), 491–497. https://doi.org/10.1093/jamia/ocz192

Reddy, S., Rogers, W., Makinen, V.-P., Coiera, E., Brown, P., Wenzel, M., Weicken, E., Ansari, S., Mathur, P., Casey, A., & Kelly, B. (2021). Evaluation framework to guide implementation of AI systems into healthcare settings. BMJ Health & Care Informatics, 28(1). https://doi.org/10.1136/ bmjhci-2021-100444

Reyes, M., Meier, R., Pereira, S., Silva, C. A., Dahlweid, F.-M., von Tengg-Kobligk, H., Summers, R. M., & Wiest, R. (2020). On the Interpretability of Artificial Intelligence in Radiology: Challenges and Opportunities. Radiology. Artificial Intelligence, 2(3), e190043. https://doi.org/10.1148/ryai.2020190043

Richardson, J. P., Smith, C., Curtis, S., Watson, S., Zhu, X., Barry, B., & Sharp, R. R. (2021). Patient apprehensions about the use of artificial intelligence in healthcare. NPJ Digital Medicine, 4(1), 140. https://doi.org/10.1038/s41746-021-00509-1

Richardson, M. L., Garwood, E. R., Lee, Y., Li, M. D., Lo, H. S., Nagaraju, A., Nguyen, X. V., Probyn, L., Rajiah, P., Sin, J., Wasnik, A. P., & Xu, K. (2021). Noninterpretive Uses of Artificial Intelligence in Radiology. Academic Radiology, 28(9), 1225– 1235. https://doi.org/10.1016/j.acra.2020.01.012

Rockenbach, M. A. B. (2021, June 13). Multimodal AI in healthcare: Closing the gaps. CodeX. https://medium.com/codex/ multimodal-ai-in-healthcare-1f5152e83be2

Rodríguez-Ruiz, A., Krupinski, E., Mordang, J.-J., Schilling, K., Heywang-Köbrunner, S. H., Sechopoulos, I., & Mann, R. M. (2019). Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System. Radiology, 290(2), 305–314. https://doi.org/10.1148/radiol.2018181371

Rodriguez-Ruiz, A., Lång, K., Gubern-Merida, A., Broeders, M., Gennaro, G., Clauser, P., Helbich, T. H., Chevalier, M., Tan, T., Mertelmeier, T., Wallis, M. G., Andersson, I., Zackrisson, S., Mann, R. M., & Sechopoulos, I. (2019). Stand- Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists. Journal of the National Cancer Institute, 111(9), 916–922. https://doi.org/10.1093/jnci/djy222

Santomartino, S. M., & Yi, P. H. (2022). Systematic Review of Radiologist and Medical Student Attitudes on the Role and Impact of AI in Radiology. Academic Radiology. https://doi.org/10.1016/j.acra.2021.12.032

Schemmel, A., Lee, M., Hanley, T., Pooler, B. D., Kennedy, T., Field, A., Wiegmann, D., & Yu, J.-P. J. (2016). Radiology Workflow Disruptors: A Detailed Analysis. Journal of the American College of Radiology: JACR, 13(10), 1210–1214. https://doi.org/10.1016/j.jacr.2016.04.009

Schreiber-Zinaman, J., & Rosenkrantz, A. B. (2017). Frequency and reasons for extra sequences in clinical abdominal MRI examinations. Abdominal Radiology (New York), 42(1), 306–311. https://doi.org/10.1007/s00261-016-0877-6

Scott, I. A., Carter, S. M., & Coiera, E. (2021). Exploring stakeholder attitudes towards AI in clinical practice. BMJ Health & Care Informatics, 28(1). https://doi.org/10.1136/ bmjhci-2021-100450

Seah, J. C. Y., Tang, C. H. M., Buchlak, Q. D., Holt, X. G., Wardman, J. B., Aimoldin, A., Esmaili, N., Ahmad, H., Pham, H., Lambert, J. F., Hachey, B., Hogg, S. J. F., Johnston, B. P., Bennett, C., Oakden-Rayner, L., Brotchie, P., & Jones, C. M. (2021). Effect of a comprehensive deep-learning model on the accuracy of chest x-ray interpretation by radiologists: a retrospective, multireader multicase study. The Lancet. Digital Health, 3(8), e496–e506. https://doi.org/10.1016/S2589- 7500(21)00106-0

Sectra Amplifier Marketplace. (2021, July 5). Sectra Medical. https://medical.sectra.com/product/sectra-amplifier- marketplace/

Sermesant, M., Delingette, H., Cochet, H., Jaïs, P., & Ayache, N. (2021). Applications of artificial intelligence in cardiovascular imaging. Nature Reviews. Cardiology, 18(8), 600–609. https://doi.org/10.1038/s41569-021-00527-2

Setio, A. A. A., Traverso, A., de Bel, T., Berens, M. S. N., van den Bogaard, C., Cerello, P., Chen, H., Dou, Q., Fantacci, M. E., Geurts, B., Gugten, R. van der, Heng, P. A., Jansen, B., de Kaste, M. M. J., Kotov, V., Lin, J. Y.-H., Manders, J. T. M. C., Sóñora-Mengana, A., García-Naranjo, J. C., … Jacobs, C. (2017). Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge. Medical Image Analysis, 42, 1–13. https://doi.org/10.1016/j.media.2017.06.015

Seyyed-Kalantari, L., Zhang, H., McDermott, M. B. A., Chen, I. Y., & Ghassemi, M. (2021). Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under- served patient populations. Nature Medicine, 27(12), 2176– 2182. https://doi.org/10.1038/s41591-021-01595-0

Shan, H., Padole, A., Homayounieh, F., Kruger, U., Khera, R. D., Nitiwarangkul, C., Kalra, M. K., & Wang, G. (2019). Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction. Nature Machine Intelligence, 1(6), 269–276. https://doi.org/10.1038/s42256-019-0057-9

Sharma, K., Rupprecht, C., Caroli, A., Aparicio, M. C., Remuzzi, A., Baust, M., & Navab, N. (2017). Automatic Segmentation of Kidneys using Deep Learning for Total Kidney Volume Quantification in Autosomal Dominant Polycystic Kidney Disease. Scientific Reports, 7(1), 2049. https://doi.org/10.1038/s41598-017-01779-0

Shelmerdine, S. C., Arthurs, O. J., Denniston, A., & Sebire, N. J. (2021). Review of study reporting guidelines for clinical studies using artificial intelligence in healthcare. BMJ Health & Care Informatics, 28(1). https://doi.org/10.1136/ bmjhci-2021-100385

Shinagare, A. B., Ip, I. K., Abbett, S. K., Hanson, R., Seltzer, S. E., & Khorasani, R. (2014). Inpatient imaging utilization: trends of the past decade. AJR. American Journal of Roentgenology, 202(3), W277–W283. https://doi.org/10.2214/AJR.13.10986

Shlobin, N. A., Baig, A. A., Waqas, M., Patel, T. R., Dossani, R. H., Wilson, M., Cappuzzo, J. M., Siddiqui, A. H., Tutino, V. M., & Levy, E. I. (2022). Artificial Intelligence for Large-Vessel Occlusion Stroke: A Systematic Review. World Neurosurgery, 159, 207–220.e1. https://doi.org/10.1016/j.wneu.2021.12.004

Silberg, J., & Manyika, J. (2019, June 6). Tackling bias in artificial intelligence (and in humans). McKinsey & Company. https://www.mckinsey.com/featured-insights/artificial- intelligence/tackling-bias-in-artificial-intelligence-and-in- humans

Singh, S., Kalra, M. K., Hsieh, J., Licato, P. E., Do, S., Pien, H. H., & Blake, M. A. (2010). Abdominal CT: comparison of adaptive statistical iterative and filtered back projection reconstruction techniques. Radiology, 257(2), 373–383. https://doi.org/10.1148/radiol.10092212

Smith-Bindman, R., Kwan, M. L., Marlow, E. C., Theis, M. K., Bolch, W., Cheng, S. Y., Bowles, E. J. A., Duncan, J. R.,
Greenlee, R. T., Kushi, L. H., Pole, J. D., Rahm, A. K., Stout, N. K., Weinmann, S., & Miglioretti, D. L. (2019). Trends in Use of Medical Imaging in US Health Care Systems and in Ontario, Canada, 2000-2016. JAMA: The Journal of the American Medical Association, 322(9), 843–856. https://doi.org/10.1001/ jama.2019.11456

Sutherland, G., Russell, N., Gibbard, R., & Dobrescu, A. (n.d.). The value of radiology, part II. https://car.ca/wp-content/ uploads/2019/07/value-of-radiology-part-2-en.pdf

Tamada, D., Kromrey, M.-L., Ichikawa, S., Onishi, H., & Motosugi, U. (2020). Motion Artifact Reduction Using a Convolutional Neural Network for Dynamic Contrast Enhanced MR Imaging of the Liver. Magnetic Resonance in Medical Sciences: MRMS: An Official Journal of Japan Society of Magnetic Resonance in Medicine, 19(1), 64–76. https://doi.org/10.2463/ mrms.mp.2018-0156

The Medical Futurist. (n.d.). The Medical Futurist. Retrieved February 23, 2022, from https://medicalfuturist.com/fda- approved-ai-based-algorithms/

The Nuance AI Marketplace for Diagnostic Imaging. (n.d.). https://www.nuance.com/content/dam/nuance/en_us/collateral/ healthcare/data-sheet/ds-ai-marketplace-for-diagnostic- imaging-en-us.pdf

Thodberg, H. H., Kreiborg, S., Juul, A., & Pedersen, K. D. (2009). The BoneXpert method for automated determination of skeletal maturity. IEEE Transactions on Medical Imaging, 28(1), 52–66. https://doi.org/10.1109/TMI.2008.926067

Thomas, K. A., Kidziński, Ł., Halilaj, E., Fleming, S. L., Venkataraman, G. R., Oei, E. H. G., Gold, G. E., & Delp, S. L. (2020). Automated Classification of Radiographic Knee Osteoarthritis Severity Using Deep Neural Networks. Radiology. Artificial Intelligence, 2(2), e190065. https://doi.org/10.1148/ ryai.2020190065

Towards trustable machine learning. (2018). Nature Biomedical Engineering, 2(10), 709–710. https://doi.org/10.1038/ s41551-018-0315-x

Trinidad, M. G., Platt, J., & Kardia, S. L. R. (2020). The public’s comfort with sharing health data with third-party commercial companies. Humanities and Social Sciences Communications, 7(1), 1–10. https://doi.org/10.1057/s41599-020-00641-5

Trivedi, H., Mesterhazy, J., Laguna, B., Vu, T., & Sohn, J. H. (2018). Automatic Determination of the Need for Intravenous Contrast in Musculoskeletal MRI Examinations Using IBM Watson’s Natural Language Processing Algorithm. Journal of Digital Imaging, 31(2), 245–251. https://doi.org/10.1007/ s10278-017-0021-3

Tsao, D. N. (2020, July 27). AI in medical diagnostics 2020- 2030: Image recognition, players, clinical applications, forecasts: IDTechEx. https://www.idtechex.com/en/research-report/ai-in- medical-diagnostics-2020-2030-image-recognition-players- clinical-applications-forecasts/766

Tucci, V., Saary, J., & Doyle, T. E. (2022). Factors influencing trust in medical artificial intelligence for healthcare professionals: a narrative review. Journal of Medical Artificial Intelligence, 5, 4–4. https://doi.org/10.21037/jmai-21-25

Ueda, D., Yamamoto, A., Nishimori, M., Shimono, T., Doishita, S., Shimazaki, A., Katayama, Y., Fukumoto, S., Choppin, A., Shimahara, Y., & Miki, Y. (2019). Deep Learning for MR Angiography: Automated Detection of Cerebral Aneurysms. Radiology, 290(1), 187–194. https://doi.org/10.1148/ radiol.2018180901

Urakawa, T., Tanaka, Y., Goto, S., Matsuzawa, H., Watanabe, K., & Endo, N. (2019). Detecting intertrochanteric hip fractures with orthopedist-level accuracy using a deep convolutional neural network. Skeletal Radiology, 48(2), 239–244. https://doi.org/10.1007/s00256-018-3016-3

Vayena, E., & Blasimme, A. (2017). Biomedical Big Data: New Models of Control Over Access, Use and Governance. Journal of Bioethical Inquiry, 14(4), 501–513. https://doi.org/10.1007/ s11673-017-9809-6

Vayena, E., Blasimme, A., & Cohen, I. G. (2018). Machine learning in medicine: Addressing ethical challenges. PLoS Medicine, 15(11), e1002689. https://doi.org/10.1371/journal. pmed.1002689

Wang, J., Yang, F., Liu, W., Sun, J., Han, Y., Li, D., Gkoutos, G. V., Zhu, Y., & Chen, Y. (2020). Radiomic Analysis of Native T1 Mapping Images Discriminates Between MYH7 and MYBPC3- Related Hypertrophic Cardiomyopathy. Journal of Magnetic Resonance Imaging: JMRI, 52(6), 1714–1721. https://doi.org/10.1002/jmri.27209

Wang, S.-H., Tang, C., Sun, J., Yang, J., Huang, C., Phillips, P., & Zhang, Y.-D. (2018). Multiple Sclerosis Identification by 14-Layer Convolutional Neural Network With Batch Normalization, Dropout, and Stochastic Pooling. Frontiers in Neuroscience, 12, 818. https://doi.org/10.3389/fnins.2018.00818

Watanabe, A. T., Lim, V., Vu, H. X., Chim, R., Weise, E., Liu, J., Bradley, W. G., & Comstock, C. E. (2019). Improved
Cancer Detection Using Artificial Intelligence: a Retrospective Evaluation of Missed Cancers on Mammography. Journal
of Digital Imaging, 32(4), 625–637. https://doi.org/10.1007/ s10278-019-00192-5

Weikert, T., Francone, M., Abbara, S., Baessler, B., Choi, B. W., Gutberlet, M., Hecht, E. M., Loewe, C., Mousseaux, E., Natale, L., Nikolaou, K., Ordovas, K. G., Peebles, C., Prieto, C., Salgado, R., Velthuis, B., Vliegenthart, R., Bremerich, J., & Leiner, T. (2021). Machine learning in cardiovascular radiology: ESCR position statement on design requirements, quality assessment, current applications, opportunities, and challenges. European Radiology, 31(6), 3909–3922. https://doi.org/10.1007/ s00330-020-07417-0

Whittlestone, J., Nyrup, R., Alexandrova, A., Dihal, K., & Cave, S. (2019). Ethical and societal implications of algorithms, data, and artificial intelligence: a roadmap for research.
London: Nuffield Foundation. https://www.nuffieldfoundation. org/sites/default/files/files/Ethical-and-Societal-Implications-of- Data-and-AI-report-Nuffield-Foundat.pdf

WHO operational handbook on tuberculosis Module 2: Screening – Systematic screening for tuberculosis disease. (n.d.). Retrieved June 19, 2022, from https://www.who.int/ publications-detail-redirect/9789240022614

Willemink, M. J., & Noël, P. B. (2019). The evolution of image reconstruction for CT-from filtered back projection to artificial intelligence. European Radiology, 29(5), 2185–2195. https://doi.org/10.1007/s00330-018-5810-7

Winder, M., Owczarek, A. J., Chudek, J., Pilch-Kowalczyk, J., & Baron, J. (2021). Are We Overdoing It? Changes in Diagnostic Imaging Workload during the Years 2010-2020 including the Impact of the SARS-CoV-2 Pandemic. Healthcare (Basel, Switzerland), 9(11). https://doi.org/10.3390/healthcare9111557

Wong, T. T., Kazam, J. K., & Rasiej, M. J. (2019). Effect of Analytics-Driven Worklists on Musculoskeletal MRI
Interpretation Times in an Academic Setting. AJR. American Journal of Roentgenology, 1–5. https://doi.org/10.2214/ AJR.18.20434

Wu, B., Zhou, Z., Wang, J., & Wang, Y. (2018). Joint learning for pulmonary nodule segmentation, attributes and malignancy prediction. 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), 1109–1113. https://doi.org/10.1109/ISBI.2018.8363765

Wu, E., Wu, K., Daneshjou, R., Ouyang, D., Ho, D. E., & Zou, J. (2021). How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals. Nature Medicine, 27(4), 582–584. https://doi.org/10.1038/s41591-021-01312-x

Wu, G.-G., Zhou, L.-Q., Xu, J.-W., Wang, J.-Y., Wei, Q., Deng, Y.-B., Cui, X.-W., & Dietrich, C. F. (2019). Artificial intelligence in breast ultrasound. World Journal of Radiology, 11(2), 19–26. https://doi.org/10.4329/wjr.v11.i2.19

Yala, A., Schuster, T., Miles, R., Barzilay, R., & Lehman, C. (2019). A Deep Learning Model to Triage Screening Mammograms: A Simulation Study. Radiology, 293(1), 38–46. https://doi.org/10.1148/radiol.2019182908

Yasaka, K., Akai, H., Kunimatsu, A., Abe, O., & Kiryu, S. (2018). Liver Fibrosis: Deep Convolutional Neural Network for Staging by Using Gadoxetic Acid-enhanced Hepatobiliary Phase MR Images. Radiology, 287(1), 146–155. https://doi.org/10.1148/ radiol.2017171928

Yeung, K. (2018). A Study of the Implications of Advanced Digital Technologies (Including AI Systems) for the Concept of
Responsibility Within a Human Rights Framework. https://papers. ssrn.com/abstract=3286027

Yoo, Y., Tang, L. Y. W., Li, D. K. B., Metz, L., Kolind, S., Traboulsee, A. L., & Tam, R. C. (2019). Deep learning of brain lesion patterns and user-defined clinical and MRI features for predicting conversion to multiple sclerosis from clinically isolated syndrome. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 7(3), 250–259. https://doi.org/10.1080/21681163.2017.1356750

Yu, A. C., Mohajer, B., & Eng, J. (2022). External Validation of Deep Learning Algorithms for Radiologic Diagnosis: A Systematic Review. Radiology. Artificial Intelligence, 4(3), e210064. https://doi.org/10.1148/ryai.210064

Yu, J.-P. J., Kansagra, A. P., & Mongan, J. (2014). The radiologist’s workflow environment: evaluation of disruptors and potential implications. Journal of the American College of Radiology: JACR, 11(6), 589–593. https://doi.org/10.1016/j. jacr.2013.12.026

Yusuf, M., Atal, I., Li, J., Smith, P., Ravaud, P., Fergie, M., Callaghan, M., & Selfe, J. (2020). Reporting quality of studies using machine learning models for medical diagnosis: a systematic review. BMJ Open, 10(3), e034568. https://doi.org/10.1136/bmjopen-2019-034568

Yu, Y., Xie, Y., Thamm, T., Gong, E., Ouyang, J., Christensen, S., Marks, M. P., Lansberg, M. G., Albers, G. W., & Zaharchuk,
G. (2021). Tissue at Risk and Ischemic Core Estimation Using Deep Learning in Acute Stroke. AJNR. American Journal of Neuroradiology, 42(6), 1030–1037. https://doi.org/10.3174/ajnr. A7081

Yu, Y., Xie, Y., Thamm, T., Gong, E., Ouyang, J., Huang, C., Christensen, S., Marks, M. P., Lansberg, M. G., Albers, G. W., & Zaharchuk, G. (2020). Use of Deep Learning to Predict Final Ischemic Stroke Lesions From Initial Magnetic Resonance Imaging. JAMA Network Open, 3(3), e200772. https://doi.org/10.1001/jamanetworkopen.2020.0772

Zhang, Y., & Yu, H. (2018). Convolutional Neural Network Based Metal Artifact Reduction in X-Ray Computed Tomography. IEEE Transactions on Medical Imaging, 37(6), 1370–1381. https://doi.org/10.1109/TMI.2018.2823083

Zhao, B., Liu, Z., Ding, S., Liu, G., Cao, C., & Wu, H. (2022). Motion artifact correction for MR images based on convolutional neural network. Optoelectronics Letters, 18(1), 54–58. https://doi.org/10.1007/s11801-022-1084-z

Zhao, J., Huang, Y., Song, Y., Xie, D., Hu, M., Qiu, H., & Chu, J. (2020). Diagnostic accuracy and potential covariates for machine learning to identify IDH mutations in glioma patients: evidence from a meta-analysis. European Radiology, 30(8), 4664–4674. https://doi.org/10.1007/s00330-020-06717-9

Zhou, C., Ding, C., Wang, X., Lu, Z., & Tao, D. (2020). One-pass Multi-task Networks with Cross-task Guided Attention for Brain Tumor Segmentation. IEEE Transactions on Image Processing: A Publication of the IEEE Signal Processing Society. https://doi.org/10.1109/TIP.2020.2973510

Zicari, R. V., Brodersen, J., Brusseau, J., Düdder, B., Eichhorn, T., Ivanov, T., Kararigas, G., Kringen, P., McCullough, M., Möslein, F., Mushtaq, N., Roig, G., Stürtz, N., Tolle, K., Tithi, J. J., van Halem, I., & Westerlund, M. (2021). Z-Inspection®: A Process to Assess Trustworthy AI. IEEE Transactions on Technology and Society, 1–1. https://doi.org/10.1109/TTS.2021.3066209

Explore Your Local Site

Applications en Neuroimagerie de l’intelligence artificielle

Hémorragie intracrânienne

AVC ischémique aigu

Lésion cérébrale traumatique

Maladies neurodégénératives

Sclérose en plaques

Neuro-oncologie

Conclusion

Références

Guide to Artificial Intelligence in Radiology

Artificial Intelligence
Imaging Applications
in Neuroradiology

Explore Your Local Site

Applications en Neuroimagerie de l’intelligence artificielle

Hémorragie intracrânienne

AVC ischémique aigu

Lésion cérébrale traumatique

Maladies neurodégénératives

Sclérose en plaques

Neuro-oncologie

Conclusion

Références

Guide to Artificial Intelligence in Radiology

Artificial Intelligence Imaging Applications in Neuroradiology

Artificial Intelligence
Imaging Applications
in Neuroradiology