Implementation of deep learning-based gauze detection in pancreatic laparoscopic and robotic surgery: an exploratory multicenter evaluation

Aim: Retained gauze is a serious complication in laparoscopic and robotic surgery. Deep learning has been applied to target detection of surgical instruments, but research on gauze detection remains scarce. This study aimed to develop a deep learning algorithm for gauze detection in pancreatic surgery using a large multicenter dataset and to assess cross-center performance variation and the effect of incorporating data from multiple clinical centers. Methods: We collected 581 videos comprising 296,127 frames from five clinical centers. A modified You Only Look Once-v3 (YOLOv3) model was designed for detection. A baseline model was trained using data from a single center, after which data from additional centers were sequentially incorporated to test cross-center performance and determine optimal data volume. Real-time testing was subsequently conducted during laparoscopic surgeries. Results: In the test set combining different centers, the baseline model exhibited a sensitivity rate (SR) of 84.70% and a precision rate (PR) of 79.58%. Incorporating data from multiple centers improved the SR and PR to 89.76% and 81.03%, respectively. Data volume experiments revealed that the improvement in sensitivity became less pronounced after 25,000 images. The model achieved a computation speed of 29.04 ms per frame, facilitating effective real-time application without surgical delay. Conclusion: The developed model achieved efficient surgical gauze detection and enabled real-time gauze tracing in clinical settings. Incorporating data from multiple centers was associated with improved performance on the corresponding evaluated test sets, supporting the feasibility of multicenter training and exploratory cross-center evaluation.