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BoneView (Detection function)

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  • 20% increased sensitivity to fracture detection.1
  • Up to 36% reduction in reading time.1
  • 11.5% increased sensitivity to multi-fractures.2

BoneView Detection function is a software-only device designed to assist clinicians in the interpretation of X-ray radiographs. BoneViewDetection function is a software as a service that takes as an input X-ray radiographs in DICOM format, processes them using artificial intelligence (deep learning), and provides as an output the detection of anomalies.​

BoneView detects the following:​

  • Fracture​
  • Dislocation
  • ​Effusion
  • ​Lesion​

BoneView takes in consideration the following areas:

  • Limbs​
  • Pelvis
  • ​Ribs
  • ​Dorsolumbar Spine3

BoneView Output

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    • CR / DX / DR Exams​
    • A body part algorithm will define which images can be processed
    • The result files provided by BoneView are in an additional series in the original study including:​

    • The “Summary Table”: a Summary synthesizing the results of BoneView at the study level. The table includes the following information:​

      • Result Code (Positive, Doubt, Negative, Not available)​
      • Number of received and analysed images​
      • List of anomalies​

      • The “Result Images”: a copy of each original analysed image with additional information from BoneView.
​
    • On each image analyzed by BoneView, an area of interest (a suspicion of an anomaly) can be signalled in 2 different ways:​

      • Solid line box when the probability of an anomaly is high on this area of interest. It is equivalent to POSITIVE.​
      • Dotted line box when there is a doubt on the presence of an anomaly in the area of interest. It is equivalent to DOUBT and the user is encouraged to carefully inspect the area.​

    • The total number of areas of interest detected by BoneView on the image (sum of POSITIVE and DOUBT areas) will be reminded in the lower-right corner of the image.​

      EU risk class and CE marking
      BoneView has CE marking (CE2797) and EU MDR class IIa

      Reimbursement status
      Not reimbursed

      Contraindications

      • BoneView is not intended to be used for X-ray radiographs from the skull and cervical spine regions.​
      • BoneView is not intended to be used on other exam modalities than X-rays.​ 
      • BoneView is not intended to be used on lossy exams.

      Target Population
 

      • The intended patients are persons admitted to a hospital/health institution for X-rays examination: while patients don't directly interact with BoneView, the results from the X-ray radiograph they just underwent are processed via BoneView and will be used by the clinicians in their diagnosis.

      Limitations

      • BoneView/ChestView only provides preliminary data, the final diagnosis shall rely upon the physician’s expertise. ​
      • BoneView/ChestView is intended for use as a concurrent reading aid during the interpretations of radiographs.​
      • Particular attention needs to be provided to the exam where:​
        • Bone View/ChestView has not received or analyzed all the images​
        • BoneView/ChestView did not provide results on all the image
        • The results from BoneView/ChestView are different images from the original images​
      • In the case of an update of the PACS or RIS solution, Gleamer shall be contacted to ensure the continued compatibility of BoneView with the updated software
        1.
        Canoni-Meynet L, Verdot P, Danner A, Calame P, Aubry S. Added value of an artificial intelligence solution for fracture detection in the radiologist's daily trauma emergencies workflow. Diagn Interv Imaging. 2022 Dec;103(12):594-600. doi: 10.1016/j.diii.2022.06.004. Epub 2022 Jun 29. PMID: 35780054.​
        2.
        Guermazi A, Tannoury C, Kompel AJ, Murakami AM, Ducarouge A, Gillibert A, Li X, Tournier A, Lahoud Y, Jarraya M, Lacave E, Rahimi H, Pourchot A, Parisien RL, Merritt AC, Comeau D, Regnard NE, Hayashi D. Improving Radiographic Fracture Recognition Performance and Efficiency Using Artificial Intelligence. Radiology. 2022 Mar;302(3):627-636. doi: 10.1148/radiol.210937. Epub 2021 Dec 21. PMID: 34931859.​
        3.
        Instructions for Use BoneView Detection, software version 2.5.x, dated 2024-04​
        4.
        Regnard NE, Lanseur B, Ventre J, Ducarouge A, Clovis L, Lassalle L, Lacave E, Grandjean A, Lambert A, Dallaudière B, Feydy A. Assessment of performances of a deep learning algorithm for the detection of limbs and pelvic fractures, dislocations, focal bone lesions, and elbow effusions on trauma X-rays. Eur J Radiol. 2022 Sep;154:110447. doi: 10.1016/j.ejrad.2022.110447. Epub 2022 Jul 22. PMID: 35921795.​
        5.
        Duron L, Ducarouge A, Gillibert A, Lainé J, Allouche C, Cherel N, Zhang Z, Nitche N, Lacave E, Pourchot A, Felter A, Lassalle L, Regnard NE, Feydy A. Assessment of an AI Aid in Detection of Adult Appendicular Skeletal Fractures by Emergency Physicians and Radiologists: A Multicenter Cross-sectional Diagnostic Study. Radiology. 2021 Jul;300(1):120-129. doi: 10.1148/radiol.2021203886. Epub 2021 May 4. PMID: 33944629.

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