Deep learning-based real-time intraoperative detection of thoracic duct.

Background: The thoracic duct (TD) is fragile in esophageal cancer surgery, and its injury may cause deadly chylothorax. Its identification is critical to prevent chylothorax and ensure patient safety. Deep learning-based image processing software may assist surgeons. This study aimed to develop and validate a deep learning-based system for real-time intraoperative detection of the thoracic duct. Methods: From 30 thoracoscopic esophagectomy videos (prone position), 2,500 images (1,400 TD annotations; 1,100 background images) were extracted. The dataset was divided into training/validation (2,000/500 images) and 40 test images from 5 independent videos. The YOLOv5-seg model's performance was evaluated via dice coefficient & intersection over union and was statistically compared with resident and attending surgeons. Results: The artificial intelligence (AI) model's segmentation performance was superior to that of resident surgeons but inferior to that of attending surgeons. The AI system achieved a 92.5% accuracy, with a mean dice coefficient of 0.677 and an intersection over union (IoU) of 0.569. The AI system demonstrated significantly lower Dice performance than attending surgeons (0.677 Conclusions: This study demonstrates the technical feasibility of a deep learning-based real-time TD segmentation system. The model achieved segmentation performance superior to that of resident surgeons but inferior to that of attending surgeons and suggests its potential as a technical foundation for future AI-guided intraoperative applications.