BoneView
Added value of artificial intelligence for the detection of pelvic and hip fractures
Anthony Jaillat, Catherine Cyteval, Marie-Pierre Baron Sarrabere, Hamza Ghomrani, Yoav Maman, Yann Thouvenin, Maxime Pastor
Data
940 radiographs from adult patients with pelvic or hip trauma
Design
Retrospective study
Junior radiologist analysed the radiographs without and with AI (3-month washout)
MSK imaging radiologists, junior radiologist and emergency physicians analyzed 10% of the radiographs first without AI, then with AI (100 exams)
Ground truth established by either CT scan, confirmation by radiography or regular follow-up at the hospital
Results
940 patients with pelvic X-rays, 539 patients with at least one fracture
Older population with a mean age = 83 years old
In total, 633 pelvic fractures (64.8% from hip, 35.2% from pelvic ring)
Good standalone performance of AI across all pelvic fractures (sens = 81%, spec = 78%)
AI improved the junior radiologist’s sensitivity by 8.39% (Rel Diff) without decreasing specificity
AI most significantly improved the performance of emergency physicians with an average increase in sensitivity of 29% without any loss of specificity
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BoneView
Early budget impact analysis of AI to support the review of radiographic examinations for suspected fractures in NHS emergency departments (ED)
Lucy Gregory, Trishal Boodhna, Mathew Storey, Susan Shelmerdine, Alex Novak, David Lowe, Hugh Harvey
BoneView
Improving fracture detection with artificial intelligence
Ana Capelastegui Alber, Roque Oca Pernas, Gotzon Iglesias Hidalgo, Jose Alejandro Larena, Miguel Angel Ulibarrena Saiz
BoneView
AI-powered solution for bone trauma