Orthopedic surgeon level joint angle assessment with artificial intelligence based on photography: a pilot study.

ABSTRACT: Accurate assessment of shoulder range of motion (ROM) is crucial for evaluating patient progress. Traditional manual goniometry often lacks precision and is subject to inter-observer variability, especially in measuring shoulder internal rotation (IR). This study introduces an artificial intelligence (AI)-based approach that uses clinical photography to improve the accuracy of ROM quantification. We analyzed a total of 150 clinical photographs, including 100 shoulder and 50 elbow images, taken between January and April 2022. An MMPose model with an HR-NET backbone architecture, pre-trained on the COCO-WholeBody dataset, was used to detect 17 anatomical landmarks. A random forest classifier (PoseRF) then categorized poses, and ROM angles were calculated. Concurrently, two clinicians indepen

SUBMITTER: Ryu SM 

PROVIDER: S-EPMC11703788 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature