Real-Time Surgical Field Segmentation & Motion Prediction via Adaptive Multi-Modal Fusion and Uncertainty-Aware Reinforcement Learning

**Abstract:** This paper presents a novel approach to enhance real-time surgical field segmentation and motion prediction for robotic surgical systems. We introduce an Adaptive Multi-Modal Fusion (AMMF) network that dynamically weighs data from multiple sensor streams—monocular stereo vision and intraoperative fluoroscopy—based on their contextual relevance. Coupled with an Uncertainty-Aware Reinforcement Learning (UARL) framework, our system learns to anticipate surgical instrument movements with improved accuracy and robustness, explicitly modeling uncertainty in both perception and control. The proposed method offers a significant advancement in robotic surgery by providing surgeons with augmented perception and predictive capabilities, leading to increased precision and reduced operative time.