Action-grounded tissue affordance enables anticipatory auto-framing that lowers surgeon cognitive workload during laparoscopic surgery
Authors: Jiayu Gu, Yiwei Wang, Jie Zhang, Guojun Cao, Keshen Lyu, Song Zhou, Yimeng Chen, Haorui Wang, +9 more
Organizations: Department of Hepatobiliary Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China · School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China · Institute of Medical Equipment Science and Engineering, Huazhong University of Science and Technology, Wuhan, China · Department of Psychology, Sun Yat-sen University, Guangzhou, China · Institute of Biomedical Engineering, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China · Department of Hepatobiliary and Pancreatic Surgery, Renmin Hospital of Wuhan University, Wuhan, China
Computational attention models could help surgeons manage the visual demands of laparoscopy, but they require dense spatial labels that are difficult to obtain because surgical intent is highly specialized and tacit. Here, we introduce DiffeoAfford, an action-grounded tissue affordance framework that retrospectively derives visual attention supervision from completed surgical procedures. By combining diffeomorphism-constrained tissue tracking with instrument trajectory analysis, DiffeoAfford generates affordance hotspot labels without manual per-frame annotation. A real-time prediction model trained on these labels anticipates relevant surgical regions and enables AffordView, an assistive auto-framing system for laparoscopic visualization. The proposed framework aligns with expert annotations and intraoperative surgeon gaze, and reduces surgeon cognitive workload during real-world evaluations using subjective, physiological, and behavioral measures.