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

TL;DR - DiffeoAfford is an action-grounded tissue affordance framework that mines visual-attention supervision retrospectively from completed laparoscopic procedures, powering a real-time auto-framing assistant (AffordView) that measurably lowers surgeon cognitive workload. It matters because it sidesteps the dense, expert-tacit spatial annotation bottleneck that has blocked surgical attention models.

• Labels are generated automatically by combining diffeomorphism-constrained tissue tracking with instrument trajectory analysis, yielding affordance hotspots without per-frame manual annotation.
• A real-time prediction model trained on these auto-derived labels anticipates surgically relevant regions, driving the AffordView assistive laparoscopic auto-framing system.
• Validation is reported along two axes: label/prediction agreement with expert annotations and intraoperative surgeon gaze, plus real-world workload reduction measured subjectively, physiologically, and behaviorally.
• Content is abstract-only, so specific datasets, metrics, baselines, and effect sizes are not available here.