SurGo-R1: Benchmarking and Modeling Contextual Reasoning for Operative Zone in Surgical Video
This addresses a critical problem for surgeons in minimally invasive surgery by providing AI-assisted contextual reasoning, though it is incremental as it builds on existing vision-language methods with domain-specific optimizations.
The paper tackles the challenge of identifying safe operative zones in surgical videos by integrating visual, procedural, and anatomical context, introducing a benchmark and a model that achieves a 6.6x improvement over generalist vision-language models with metrics like 32.7 mIoU and 54.8% hardcore accuracy.
Minimally invasive surgery has dramatically improved patient operative outcomes, yet identifying safe operative zones remains challenging in critical phases, requiring surgeons to integrate visual cues, procedural phase, and anatomical context under high cognitive load. Existing AI systems offer binary safety verification or static detection, ignoring the phase-dependent nature of intraoperative reasoning. We introduce ResGo, a benchmark of laparoscopic frames annotated with Go Zone bounding boxes and clinician-authored rationales covering phase, exposure quality reasoning, next action and risk reminder. We introduce evaluation metrics that treat correct grounding under incorrect phase as failures, revealing that most vision-language models cannot handle such tasks and perform poorly. We then present SurGo-R1, a model optimized via RLHF with a multi-turn phase-then-go architecture where the model first identifies the surgical phase, then generates reasoning and Go Zone coordinates conditioned on that context. On unseen procedures, SurGo-R1 achieves 76.6% phase accuracy, 32.7 mIoU, and 54.8% hardcore accuracy, a 6.6$\times$ improvement over the mainstream generalist VLMs. Code, model and benchmark will be available at https://github.com/jinlab-imvr/SurGo-R1