Artificial intelligence-guided clavicle fracture detection on plain radiographs: A retrospective diagnostic accuracy study

Extensive research exists on artificial intelligence (AI)-assisted fracture detection in various anatomical regions, but clavicle fractures remain comparatively understudied. This study aimed to compare the diagnostic performance of AI-guided clavicle fracture detection on plain radiographs with that of an experienced orthopedic surgeon. We retrospectively analyzed all clavicle radiographs obtained at our institution between September 11,2023 and October 31,2024 following the clinical implementation of AI-guided fracture detection. Radiological findings confirmed by a senior radiologist served as the reference standard. AI performance was additionally compared with the assessments of an experienced, blinded orthopedic surgeon. Diagnostic performance metrics included accuracy, sensitivity, specificity, Cohen kappa, F1 score, and Youden index. A total of 367 clavicle radiographs were included, comprising 186 anteroposterior (AP) and 181 second-plane views. The mean patient age was 44.6 ± 26.2 years, and 59.4% were male. According to the reference standard, clavicle fractures were present in 96 AP radiographs (51.6%) and 95 second-plane radiographs (52.5%). On AP radiographs, the AI system achieved an accuracy of 97.31%, sensitivity of 95.83%, specificity of 98.89%, Cohen kappa of 0.95, and F1 score of 0.97. Diagnostic performance on second-plane radiographs was similarly high and overall comparable to that of the experienced orthopedic surgeon. This retrospective diagnostic accuracy study shows that AI-guided clavicle fracture detection is highly accurate and reliable, with performance approaching that of an experienced orthopedic surgeon. These findings support the potential role of AI as a diagnostic support tool in clavicle fracture detection, while prospective and multicenter validation remains warranted.