aceDiagnostic accuracy & clinical importance of AI confidence for extremity fracture detection: 2,508-patient retrospective cohort
Purpose To estimate diagnostic performance of a deep learning algorithm for extremity fracture detection on radiographs in patients aged ≥ 2 years using a refined reference standard. Secondary, to compare positive predictive value (PPV) by the algorithm's built-in confidence level (high vs. low) and diagnostic performance between adults (≥ 18 years) and children. Methods This retrospective single-center study consecutively included patients with radiography of a suspected extremity fracture between January and D…
PPV for high-confidence was superior to low-confidence detections (99.1% vs 59.1%, p Conclusion The algorithm showed good diagnostic performance for extremity fracture detection on radiographs.
Evidence
- Peer-reviewedEmergency Radiology2026-09-22
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Truvace Impact Record TRV-2026-1176, v1: “Diagnostic accuracy & clinical importance of AI confidence for extremity fracture detection: 2,508-patient retrospective cohort.” Truvace, 2026-09-23. /record/TRV-2026-1176 (accessed at citation time). sha256 cc2d9e7d627a39d6…
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