Deep learning-based screening for locomotive syndrome using single-camera walking video: Development and validation study

Locomotive Syndrome (LS) is defined by decreased walking and standing abilities due to musculoskeletal issues. Early diagnosis is vital as LS can be reversed with appropriate intervention. Although diagnosing LS using standardized charts is straightforward, the labor-intensive and time-consuming nature of the process limits its widespread implementation. To address this, we introduced a Deep Learning (DL)-based computer vision model that employs OpenPose for pose estimation and MS-G3D for spatial-temporal graph analysis. This model objectively assesses gait patterns through single-camera video captures, offering a novel and efficient method for LS prediction and analysis. Our model was trained and validated using a dataset of 186 walking videos, plus 65 additional videos for external validation. The model achieved an average sensitivity of 0.86, demonstrating high effectiveness in identifying individuals with LS. The model’s positive predictive value was 0.85, affirming its reliable LS detection, and it reached an overall accuracy rate of 0.77. External validation using an independent dataset confirmed strong generalizability with an Area Under the Curve of 0.75. Although the model accurately diagnosed LS cases, it was less precise in identifying non-LS cases. This study pioneers in diagnosing LS using computer vision technology for pose estimation. Our accessible, non-invasive model serves as a tool that can accurately diagnose the labor-intensive LS tests using only visual assessments, streamlining LS detection and expediting treatment initiation. This significantly improves patient outcomes and marks a crucial advancement in digital health, addressing key challenges in management and care of LS.Author summary: Locomotive syndrome (LS) is a condition in which problems with bones, joints, muscles, and nerves cause a decline in the ability to walk and stand. It is estimated that more than 45 million people in Japan have LS. Early detection is vital because LS can be reversed with early treatment. Detecting LS using widely used diagnostic criteria is easy but labor-intensive and time-consuming and, therefore, not widespread enough. To solve this problem, we developed an artificial intelligence model to detect LS by capturing gait videos. Our artificial intelligence model performed as well as or better than orthopedic surgeons in diagnostic accuracy (accuracy: 72% in our artificial intelligence model vs 52% in the average of 6 different orthopedic doctors’ clinical diagnosis), but often diagnosed non-LS cases as LS. This non-invasive artificial intelligence model serves as an accurate and simple diagnostic tool for the LS examination, thereby accelerating the timing of behavioral change and treatment intervention. Our model will significantly improve patients’ quality of life and enhance the management and care of LS.