Algorithmic assessment of shoulder function using smartphone video capture and machine learning

• Published in: Scientific reports Vol. 13; no. 1; pp. 19986 - 14
• Main Authors: Darevsky, David M., Hu, Daniel A., Gomez, Francisco A., Davies, Michael R., Liu, Xuhui, Feeley, Brian T.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 15.11.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/1314; 631/1647/48; 692/4023/1671/1668; 692/4023/1671/1835; 692/53/2421; Aged; Animal models; Animals; Humanities and Social Sciences; Humans; Injuries; Kinematics; Machine Learning; Mice; Motion capture; multidisciplinary; Muscles; Neural networks; Older people; Range of Motion, Articular - physiology; Rotator Cuff; Rotator Cuff Injuries - diagnosis; Science; Science (multidisciplinary); Shoulder; Shoulder Injuries; Smartphone; Smartphones
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-46966-4

Tears within the stabilizing muscles of the shoulder, known as the rotator cuff (RC), are the most common cause of shoulder pain—often presenting in older patients and requiring expensive advanced imaging for diagnosis. Despite the high prevalence of RC tears within the elderly population, there is no previously published work examining shoulder kinematics using markerless motion capture in the context of shoulder injury. Here we show that a simple string pulling behavior task, where subjects pull a string using hand-over-hand motions, provides a reliable readout of shoulder mobility across animals and humans. We find that both mice and humans with RC tears exhibit decreased movement amplitude, prolonged movement time, and quantitative changes in waveform shape during string pulling task performance. In rodents, we further note the degradation of low dimensional, temporally coordinated movements after injury. Furthermore, a logistic regression model built on our biomarker ensemble succeeds in classifying human patients as having a RC tear with > 90% accuracy. Our results demonstrate how a combined framework bridging animal models, motion capture, convolutional neural networks, and algorithmic assessment of movement quality enables future research into the development of smartphone-based, at-home diagnostic tests for shoulder injury.