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

ABSTRACT: 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 p

SUBMITTER: Darevsky DM 

PROVIDER: S-EPMC10652003 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature