American journal of physical medicine & rehabilitation · 2026
Artificial Intelligence Segmentation In Musculoskeletal Ultrasound: A Novel Study Across Five Joints Using Expert Ground Truth.
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Tok Fatih, Dakkak Michael, Schwartz Gary S, Kronenfeld Mark, Özçakar Levent
Аннотация
OBJECTIVE: To evaluate the performance of an artificial intelligence (AI) technology in recognizing and segmenting anatomical structures across five joints (shoulder, elbow, wrist/hand, knee, and ankle/foot) in musculoskeletal ultrasound (US).
DESIGN: This cross-sectional study included 79 adults. Software segmentations were compared to ground truth annotations provided by four experts. Performance was assessed using positive predictive agreement (PPA) and negative predictive agreement (NPA), with a true/false threshold defined as a dice similarity coefficient (DSC) ≥0.50. Subjects with and without sonographic disorders were evaluated to assess robustness.
RESULTS: 3017 cine clips (15600 frames) covering 120 anatomical structures were analyzed. PPA and NPA exceeded 0.90 for most structures; tendons, muscles, and nerves showed near-perfect agreement (≥0.97). Similar performance was observed in healthy subjects (PPA: 0.93-0.96; NPA: 0.94-0.99) and subjects with sonographic disorders (PPA: 0.91-0.96; NPA: 0.91-0.99) (P<0.001).
CONCLUSION: This first large-scale study demonstrates the high accuracy and reliability of AI-based segmentation. By improving anatomical identification consistency during image acquisition, the software may help reduce technical operator dependency. The results further indicate strong potential for real-time assistance, supporting clinicians and trainees, and underscore the broader applicability of AI-based software in musculoskeletal US.
DESIGN: This cross-sectional study included 79 adults. Software segmentations were compared to ground truth annotations provided by four experts. Performance was assessed using positive predictive agreement (PPA) and negative predictive agreement (NPA), with a true/false threshold defined as a dice similarity coefficient (DSC) ≥0.50. Subjects with and without sonographic disorders were evaluated to assess robustness.
RESULTS: 3017 cine clips (15600 frames) covering 120 anatomical structures were analyzed. PPA and NPA exceeded 0.90 for most structures; tendons, muscles, and nerves showed near-perfect agreement (≥0.97). Similar performance was observed in healthy subjects (PPA: 0.93-0.96; NPA: 0.94-0.99) and subjects with sonographic disorders (PPA: 0.91-0.96; NPA: 0.91-0.99) (P<0.001).
CONCLUSION: This first large-scale study demonstrates the high accuracy and reliability of AI-based segmentation. By improving anatomical identification consistency during image acquisition, the software may help reduce technical operator dependency. The results further indicate strong potential for real-time assistance, supporting clinicians and trainees, and underscore the broader applicability of AI-based software in musculoskeletal US.