Department of General Surgery, School of Medicine, Kocaeli University, Kocaeli, Turkey.
Faculty of Technology, Information Systems Engineering, Kocaeli University, Kocaeli, Turkey.
Int J Med Robot. 2024 Feb;20(1):e2620. doi: 10.1002/rcs.2620.
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.
在处理肠梗阻时，快速准确的决策至关重要。本研究旨在结合深度学习和外科专业知识，以提高肠梗阻病例的决策能力。
我们基于 YOLOv8 框架开发了一个深度学习模型，该模型在一个包含 700 张图像的数据集上进行了训练，这些图像分为手术和非手术组，以手术结果作为真实情况。通过标准指标评估了模型的性能。
在置信度阈值为 0.5 时，该模型的敏感性为 83.33%，特异性为 78.26%，精度为 81.7%，召回率为 75.1%，mAP@0.5 为 0.831。
该模型在区分手术和非手术管理病例方面表现出了良好的效果。深度学习与外科专业知识的融合丰富了肠梗阻管理的决策过程。该模型可以帮助外科医生在复杂的情况下，如肠梗阻管理，促进技术和临床洞察力的协同作用，以提高患者的护理水平。
