A Tool for Low-Cost, Quantitative Assessment of Shoulder Function Using Machine Learning.

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 are no accessible and low-cost methods to assess shoulder function which can eschew the barrier of an in-person physical exam or imaging study. 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 health 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 predictive 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 task kinematics, machine learning, and algorithmic assessment of movement quality enables future development of smartphone-based, at-home diagnostic tests for shoulder injury.