Video Based Assessment of Surgical Skills Using Frozen Pretrained Video Foundation Models

Automated video-based surgical skill assessment has advanced rapidly, yet rigorous evaluation of continuous standardized score prediction under participant-level generalization to unseen trainees remains limited. We introduce VBA-Net+, a video-only framework for Fundamentals of Laparoscopic Surgery (FLS) score regression and pass-fail classification using pretrained video foundation models as frozen feature extractors. We evaluate two FLS datasets, suturing and pattern cutting, using VideoPrism, V-JEPA2, and VideoMAE v2, with a frame-level SimCLR baseline. A lightweight fully convolutional head is trained on embeddings offline and evaluated using participant-level leave-one-user-out (LOUO) cross-validation within the standardized assessment protocol. For continuous score prediction, the best representation achieves $R^2$=0.6367 for suturing and 0.9261 for pattern cutting. For pass-fail classification at official FLS thresholds, area under the receiver operating characteristic curve (AUC) reaches 0.9073 and 0.9906, respectively. Frozen video-encoder pipelines generally outperformed the frame-level SimCLR pipeline, particularly for suturing, providing a benchmark for video-only FLS assessment.