SurgCast: Decoding Polar Transitions for Surgical State-Change Forecasting

Surgical state-change forecasting predicts changes in activity states within a future window from past observations. Such forecasts can support instrument preparation and intraoperative coordination. Direct categorical decoding leaves transition timing and semantic effects implicit: timing determines whether a change falls within the forecast window, while semantic effects specify which activities are added or removed. We introduce SurgCast, a structured decoder that explicitly models these complementary factors using features from a frozen visual foundation model. TransitionCast parameterizes a learned transition representation in polar coordinates, decoding its magnitude into transition occurrence probabilities over future intervals and its direction into predictions of activity additions and removals. These predictions receive separate supervision and are composed with current-state evidence over the forecast window to construct four-category state-change probabilities. The resulting probabilities are fused with base predictions, after which StateRecast refines the fused logits through an additive residual learned with the first-stage predictor fixed. SurgCast achieves state-of-the-art performance with state mAP scores of 40.33% for surgical actions on CholecT50 and 29.55% for procedural steps on GraSP, exceeding SurgFUTR-TS results by 3.93 and 7.65 percentage points, respectively. On CholecT50, SurgCast improves state mAP over the corresponding baselines by at least 1.67 percentage points across all evaluated backbone types and sizes.