PARIS, March 2, 2022 /PRNewswire/ -- GLEAMER, a French medtech company pioneering the use of artificial intelligence technology in the practice of radiology, announced today that the United States Food and Drug Administration has cleared its BoneView® AI software for use by U.S. healthcare specialists to aid in diagnosing fractures and traumatic injuries on X-rays. In a U.S. study recently published by Boston University School of Medicine, BoneView was shown to help detect and localize fractures over the entire appendicular skeleton, rib cage, thoracic and lumbar spine, improving sensitivity and specificity, while reducing reading time. BoneView received the CE mark class 2a certification in the European Union in March 2020 and has been widely adopted in more than 300 institutions across 13 countries.

GLEAMER developed BoneView to aid radiologists, orthopedic surgeons, emergency physicians, rheumatologists, family physicians and physician assistants, all of whom read X-rays in clinical practice to diagnose fractures in their patients. BoneView detects fractures in X-ray images and submits them to radiologists for final validation, providing healthcare professionals with a safe, reliable, time-saving and user-friendly tool. The BoneView AI algorithm is cleared as a CADe/CADx (computer assisted detection and diagnosis) by the FDA and highlights regions of interest with bounding boxes around areas where fractures are suspected so radiologists can prioritize reading those X-rays.

The Study conducted between July 2020 and January 2021, used images acquired in the US from multiple centers on instruments from a wide variety of manufacturers and involved readers from Boston University School of Medicine (MA), Stony Brook University Renaissance School of Medicine (NY), and Massachusetts General Hospital - Harvard Medical School (MA). Results showed that BoneView AI assistance provided a 10.4 percent improvement of fracture detection sensitivity and shortened the radiograph reading time by 6.3 seconds per patient. The BoneView AI algorithm's standalone performance for fracture detection had an AUC of .97.
Across the six types of specialists participating in the Study, the combination of AI and health professionals' interpretations lowered the false negative rate (undetected fractures) on X-rays by 29 percent, while reducing reading time by 15 percent on exams specifically selected for their difficulty. BoneView also improved the specificity of fracture detection by radiologists and non-radiologists involving many anatomical locations, including foot/ankle, knee/leg, hip/pelvis, hand/wrist, elbow/arm, shoulder/clavicle, rib cage and thoracolumbar spine.

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