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Hip fracture detection on radiographs using an artificial intelligence-based support tool: a diagnostic accuracy
Nicholas Bonde1, Kristian Kjærgaard2, Henriette Aunaas1
1Department of Orthopaedic Surgery and Traumatology, Copenhagen University Hospital, Bispebjerg, København NV, Denmark.
Acta Radiologica (Stockholm, Sweden : 1987)
|February 9, 2026
Summary
Artificial intelligence (AI) tools significantly improved junior doctors' hip fracture detection accuracy on radiographs, reducing missed fractures by 49%. This AI support enhanced diagnostic performance, aiding timely surgical intervention.
Area of Science:
- Radiology
- Medical Imaging
- Artificial Intelligence in Medicine
Background:
- Assessing subtle hip fractures on radiographs is challenging for less experienced physicians, potentially delaying diagnosis and surgery.
- Clinical artificial intelligence (AI) decision support tools show promise in improving fracture detection on radiographs.
Purpose of the Study:
- To evaluate the impact of a CE-marked AI fracture detection tool on junior doctors' diagnostic accuracy for hip fractures.
Main Methods:
- Eight junior doctors reviewed 246 hip radiographs with and without AI support.
- Diagnostic performance (sensitivity, specificity) was compared to a reference standard set by musculoskeletal radiologists.
- AI tool's standalone performance was also assessed.
Main Results:
- Mean sensitivity for hip fracture detection increased from 0.89 to 0.94 with AI support (P = 0.002), reducing false negatives by 49%.
- Specificity showed no significant change (0.90 without AI vs. 0.91 with AI).
- AI standalone performance demonstrated high sensitivity (0.99) and moderate specificity (0.73).
Conclusions:
- Seven out of eight junior doctors improved their fracture detection rates with AI assistance.
- The AI tool demonstrated a valuable performance gain for readers, enhancing diagnostic accuracy in hip fracture detection.
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