BoneView® is a revolutionary AI software designed to assist radiologists and emergency clinicians in the diagnosis of skeletal fractures. It uses advanced algorithms to detect and localise lesions on X-rays – graphically highlighting areas of interest – before submitting the images to radiologists for validation. Fujifilm X-ray systems are equipped with a new image processing box called EX-Mobile enabling to connect with BoneView® software. Results are available within 30 seconds at the point of care, providing healthcare professionals with additional support to help improve patient management.
The user-friendly software can be seamlessly integrated into Fujifilm’s comprehensive x-ray modality line-up, making it perfectly suited to small or remote clinics, pop-up medical centres, nursing homes, up to large multi-function institutions. This will aid medical staff in the rapid identification of patients with suspected fractures, triaging them for further investigation to ease workflows and enhance patient care pathways. A clinical trial involving appendicular skeletal fractures found that BoneView® reduced the number of false positives by 41.9 %, and improved fracture detection sensitivity and specificity.1 These results are supported by another study involving additional anatomical locations, where AI assistance reduced radiograph reading times by 6.3 seconds per patient.2
Richard Cahalane, Product Manager Digital Modalities, FUJIFILM Europe GmbH, explained:
Christian Allouche, CEO at GLEAMER, added:
BoneView® will be launched in Europe at the European Congress of Radiology in July.
Visit www.gleamer.ai/boneview for more information.
® BoneView is a trade mark of GLEAMER
- Duron L, Ducarouge A, Gillibert A, et al. 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;300(1):120-129. doi:10.1148/radiol.2021203886
- Guermazi A, Tannoury C, Kompel AJ, et al. Improving Radiographic Fracture Recognition Performance and Efficiency Using Artificial Intelligence. Radiology. 2022;302(3):627-636. doi:10.1148/radiol.210937