Action-grounded tissue affordance enables anticipatory auto-framing that lowers surgeon cognitive workload during laparoscopic surgery

AI 中文解读

做腹腔镜手术时，医生得盯着屏幕找病灶位置，手眼配合压力很大。现在有了一款叫DiffeoAfford的AI助手，相当于给手术镜头装了“智能导航员”。以往训练这类AI需要专家一帧帧手动标注画面，费时费力；而这项新技术能直接从已完成的手术录像中自动学会识别关键组织区域，免去了繁琐的人工标注。系统提前预判医生下一步想看哪里，自动调整镜头，让重要部位始终保持在视野中央。在实际测试中，这套自动取景系统不仅贴合医生的真实操作习惯，还显著减轻了手术时的脑力负担。对普通人来说，这意味着未来做手术时，医生能更专注、更从容地操作，降低因疲劳或分心造成失误的风险。随着这类智能辅助系统普及，手术过程会更安全，患者也能获得更稳定的治疗效果。科技正在从“替代人力”走向“读懂人心”，这正是医疗AI最值得期待的方向。

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.