Deep learning enhances ureter identification in laparoscopic surgery

A novel deep learning model, Ureternet, has demonstrated promising results in real-time ureter identification during laparoscopic colorectal surgery. Using a convolutional neural network-based approach, researchers achieved precision, recall, and dice coefficients of approximately 0.712, 0.722, and 0.716, respectively, on test data. The model operates swiftly, processing images in an average of 71 milliseconds. By potentially minimizing the risk of iatrogenic ureteral injury, Ureternet could significantly enhance surgical safety.