SurgiGard: Surgical Instrument Handover Graph-Based Supervision and Robust Detection

Efficient and safe handover of surgical instruments is crucial for optimizing operating room performance and ensuring patient safety. Traditional observational methods, such as manual annotation and direct supervision, often lack the ability to quantitatively analyze these critical interactions, leading to gaps in understanding and workflow inefficiencies. This paper introduces SurgiGard, a novel approach that leverages multimodal machine learning with the CLIP model and Neo4j graph database to precisely detect and analyze instrument handovers during surgical procedures. We developed a comprehensive dataset by collecting and annotating video footage from real surgeries, using the CLIP model to identify key events of instrument possession and transfer. By integrating these detections into a graph-based framework, SurgiGard quantifies handover frequencies, directionality, and patterns, revealing hidden bottlenecks and opportunities for procedural improvements. Our results demonstrate that this method not only accurately captures surgical handovers but also uncovers new insights into team dynamics, paving the way for targeted enhancements in surgical training and protocols. The SurgiGard methodology presents a promising step toward enhancing safety, communication, and efficiency in the operating room.