Boneview™
AI solution for standard radiography fracture detection
BoneView™ provides radiologists and emergency physicians with an instant, automatic second reading of trauma radiographs, directly in their reading environment. BoneView™ analyses radiographs and provides an assessment of the presence of fractures, dislocations, joint effusion or bone lesions at the examination scale and locates them on each image.
Detection and localization of fractures, effusions, dislocations, and bone lesions on radiological images.
Every X-ray image of an examination is enriched with enclosing boxes around the region where a lesion is present or suspected to be present.
Classification and screening of examinations in three categories: presence, doubt, or absence of lesion.
This automatic triage of cases into three categories, and therefore rapid identification of normal cases can be presented directly at the patient list level.
Seamlessly integrated results into the PACS through the Incepto Gateway. No additional interface required.
Save time and improve reading comfort
Reduce medical errors and improve diagnostic accuracy
Reduce missed fractures claims and associated costs
Save time, secure your diagnosis and optimize your workflow with Incepto
Publications
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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, N. Regnard et al., European Journal of Radiology, 2022
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Added value of an artificial intelligence solution for fracture detection in the radiologist’s daily trauma emergencies workflow, L. Canoni-Meynet, et al., Diagnostic and Interventional Imaging, 2022
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Automated detection of acute appendicular skeletal fractures in pediatric patients using deep learning. Hayashi, D. et al. Skeletal Radiol., 2022
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Assessment of an AI Aid in Detection of Adult Appendicular Skeletal Fractures by Emergency Physicians and Radiologists: A Multicenter Cross-sectional Diagnostic Study. L. Duron, et al. Radiology 2021 300:1, 120-129
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Effectiveness of an Artificial Intelligence Software for Limb Radiographic Fracture Recognition in an Emergency Department. Herpe, G.; Nelken, H.; Vendeuvre, T.; Guenezan, J.; Giraud, C.; Mimoz, O.; Feydy, A.; Tasu, J.-P.; Guillevin, R. t. J. Clin. Med. 2024, 13, 5575
Regulatory
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