Multi-view surgical phase recognition during laparoscopic cholecystectomy

Abstract In the realm of laparoscopic procedures, intelligent context-aware assistance systems hold promise for enhancing surgical workflows and patient safety. This study employs a multi-view approach to recognize surgical phases, combining data from a laparoscopic camera and an in-room camera simultaneously. The study aimed to improve phase recognition accuracy using a Transformer-based model with late sensor fusion, which yielded mixed results. The data poses significant challenges, as self-recorded videos are insufficient for extracting relevant information, necessitating real-world data. Additionally, the overall model needs refinement, as certain components degrade performance with poor data. This research highlights the complexities and opportunities in integrating multiview data for surgical phase recognition, emphasizing the importance of diverse data collection strategies and model architectures for real-world surgical settings.