Explainable machine learning using EMG and accelerometer sensor data quantifies surgical skill and identifies biomarkers of expertise.

ABSTRACT: Traditional evaluations of surgical skill rely heavily on subjective assessments, limiting precision and scalability in modern surgical education. With the emergence of robotic platforms and simulation-based training, there is a pressing need for objective, interpretable, and scalable tools to assess technical proficiency in surgery. This study introduces an explainable machine learning (XAI) framework using surface electromyography (sEMG) and accelerometer data to classify surgeon skill levels and uncover actionable neuromuscular biomarkers of expertise. Twenty-six participants, including novices, residents, and expert urologists, performed standardized robotic tasks (suturing, knot tying, and peg transfers) while sEMG and motion data were recorded from 12 upper-extremity muscle sites usi

SUBMITTER: Soangra R 

PROVIDER: S-EPMC12789108 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature