Automated surgical OSATS prediction from videos

Abstract

The assessment of surgical skills is an essential part of medical training. The prevalent manual evaluations by expert surgeons are time consuming and often their outcomes vary substantially from one observer to another. We present a video-based framework for automated evaluation of surgical skills based on the Objective Structured Assessment of Technical Skills (OSATS) criteria. We encode the motion dynamics via frame kernel matrices, and represent the motion granularity by texture features. Linear discriminant analysis is used to derive a reduced dimensionality feature space followed by linear regression to predict OSATS skill scores. We achieve statistically significant correlation (p-value <0.01) between the ground-truth (given by domain experts) and the OSATS scores predicted by our framework.