Deep learning model accurately predicts postoperative complications

A deep learning neural network (DLNN) showed superior predictive ability for postoperative complications in laparoscopic right hemicolectomy, achieving an accuracy of 86%. Key predictors included intraoperative minimal bleeding and adherence to fast-track recovery protocols. Compared to traditional machine learning models, the DLNN outperformed decision trees and random forests, showcasing potential for enhancing patient safety and optimizing perioperative management. Future research should aim for external validation to enhance implementation across varied clinical environments.