Unveiling new patterns: A surgical deep learning model for intestinal obstruction management.

Abstract

Swift and accurate decision-making is pivotal in managing intestinal obstructions. This study aims to integrate deep learning and surgical expertise to enhance decision-making in intestinal obstruction cases. We developed a deep learning model based on the YOLOv8 framework, trained on a dataset of 700 images categorised into operated and non-operated groups, with surgical outcomes as ground truth. The model's performance was evaluated through standard metrics. At a confidence threshold of 0.5, the model demonstrated sensitivity of 83.33%, specificity of 78.26%, precision of 81.7%, recall of 75.1%, and mAP@0.5 of 0.831. The model exhibited promising outcomes in distinguishing operative and nonoperative management cases. The fusion of deep learning with surgical expertise enriches decision-making in intestinal obstruction management. The proposed model can assist surgeons in intricate scenarios such as intestinal obstruction management and promotes the synergy between technology and clinical acumen for advancing patient care.