Abstract OBJECTIVES Measuring surgical competency is essential for surgical residents to ensure patient safety. Traditional assessment tools, rely on subjective evaluation. This study evaluated whether Artificial Intelligence (AI)-based hand tracking can more objectively distinguish between levels of surgical competency and predict surgical years of experience versus traditional assessments. METHODS 44 participants, including medical students, surgical residents, and surgical consultants performed transcutaneous suturing, intracutaneous suturing, and surgical knot tying. Videos of intracutaneous suturing were scored using the OSATS. Hand movements were analysed using AI tracking software to extract coordinates to measure velocity, pathlength, and jerk. Linear regression models predicted experience years using procedural time and OSATS in combination with hand tracking metrics. RESULTS Hand tracking metrics varied mainly between medical students and more experienced groups. Traditional assessment tools (procedural time, OSATS) could predict experience years during training, with an adjusted R2 ranging from 0.537–0.638, dependent on procedure type. Hand tracking variables identified multiple significant predictors for years of experience, with an adjusted R2 of 0.540–0.712, which outperformed the traditional tools in each procedure. Combining all assessment tools (time, OSATS and hand tracking) gave the best predictive value, with an adjusted R2 ranging from 0.540–0.809), with velocity, pathlength, jerk and acceleration as significant predictors. CONCLUSIONS AI-based hand tracking provides a new method for objective, reproducible measures of surgical skills. Incorporating hand tracking metrics enhances prediction of surgical experience, and supports standardised as well as objective evaluation of skills assessment in surgical training.
Atazadah et al. (Thu,) studied this question.