Deep learning model achieves high accuracy in ureter identification

A deep learning computer vision model demonstrated precise real-time identification of the left ureter during laparoscopic sigmoidectomy, achieving a mean average precision of 0.92. Key evaluation metrics, including precision, recall, and dice coefficient, reached impressive values of 0.94, 0.88, and 0.90, respectively. Operating at 32 frames per second, the model significantly aids surgical navigation. Despite limitations such as sample size and lack of external validation, results indicate strong potential for enhancing surgical safety and outcomes.