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

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 independently measured shoulder IR at the vertebral level, and inter-observer agreement was evaluated. Linear regression analyses were conducted to correlate the AI-derived measurements with the clinicians' assessments. The AI-based algorithm accurately detected anatomical landmarks in 96% of shoulder and 100% of elbow images. Pose detection achieved 95% accuracy overall, with 100% accuracy for specific shoulder (abduction, flexion, external rotation) and elbow (flexion, extension) poses. Intraclass correlation coefficients (ICCs) between the AI algorithm and human observers ranged from 0.965 to 0.997, indicating excellent inter-observer reliability. Kruskal-Wallis test showed no statistically significant differences in ROM measurements among the AI algorithm and two human observers across all joint angles ( Supplementary Information: The online version contains supplementary material available at 10.1007/s13534-024-00432-w.