Surgical Phase Detection with Deep Learning for Thoracoscopic Pulmonary Lobectomy Surgery

Accurate recognition of surgical phases is essential for clinical education and the advancement of robotic surgery. Although significant progress has been made in automated surgical phase recognition for various procedures, a notable research gap remains in the context of video-assisted thoracoscopic pulmonary lobectomy-a highly complex and technically demanding operation. To address this gap, this study is the first to comprehensively investigate automated phase recognition in thoracoscopic pulmonary lobectomy. Our principal contributions include: (1) the creation of the first novel, expert-annotated private video dataset for pulmonary lobectomy, providing a critical resource for this understudied surgical domain; and (2) the optimization and adaptation of the SV-RCNet model, which achieves robust phase recognition by effectively capturing both visual and temporal features. Experimental results demonstrate that our approach achieves a surgical phase recognition accuracy of 92.4%, comparable to the state-of-the-art performance in other surgical phase recognition tasks. This work not only fills a critical research gap, but also lays a solid foundation for the future development of computer-assisted thoracoscopic surgery, offering significant clinical and educational value for surgical training and the advancement of intelligent robotic systems.