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arXiv · 2608.30872

SurgSkill-Bench: A Benchmark for Multimodal Surgical Skill Assessment

Chaohui Dang·Zheheng Jiang·James Glasbey·David Luke·Theodoros Arvanitis·Le Zhang
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Abstract

Objective assessment of surgical technical skill is important for surgical training and structured feedback, but current workflows remain dependent on labor-intensive expert review. Existing automated approaches primarily focus on visual inputs and provide limited support for jointly studying operative performance, structured skill scores, and evaluator feedback. We introduce SurgSkill-Bench, an initial video-score-text benchmark-style dataset containing 214 surgical training simulation videos, six-dimensional OSATS scores, and expert free-text comments. We define two evaluation settings: video-only OSATS prediction for automated assessment and post hoc expert-comment-assisted prediction, where evaluator comments are available as auxiliary information. We provide controlled baseline experiments using representative frozen visual backbones, content-adaptive key-frame sampling, and a simple video-text co-attention fusion module. Under internal video-level validation, content-adaptive sampling improves video-only performance in this dataset, while evaluator comments provide additional score-related signal in the assisted setting. The best mean AUROC reaches 0.88 under dataset-specific median dichotomization. We further discuss evaluation constraints related to dataset scale, metadata completeness, and the interpretation of comment-assisted prediction. Code will be released publicly at a later date.

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Chaohui Dang, Zheheng Jiang, James Glasbey, David Luke, Theodoros Arvanitis, Le Zhang. 2026-08-31. SurgSkill-Bench: A Benchmark for Multimodal Surgical Skill Assessment. https://arxiv.org/abs/2608.30872

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