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

IF 2.3 3区医学 Q2 SURGERY

International Journal of Medical Robotics and Computer Assisted Surgery Pub Date : 2024-01-06 DOI:10.1002/rcs.2620

Ozan Can Tatar, Mustafa Alper Akay, Elif Tatar, Semih Metin

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引用次数: 0

Abstract

Background

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.

Methods

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.

Results

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 [email protected] of 0.831.

Conclusions

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.

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揭示新模式：用于肠梗阻管理的外科深度学习模型

背景迅速而准确的决策是处理肠梗阻的关键。本研究旨在整合深度学习和外科专业知识，以提高肠梗阻病例的决策水平。方法我们在 YOLOv8 框架的基础上开发了一个深度学习模型，并在一个包含 700 张图像的数据集上进行了训练，这些图像被分为手术组和非手术组，并将手术结果作为基本事实。该模型的性能通过标准指标进行评估。结果在置信度阈值为 0.5 时，该模型的灵敏度为 83.33%，特异度为 78.26%，精确度为 81.7%，召回率为 75.1%，[email protected] 为 0.831。结论该模型在区分手术和非手术治疗病例方面表现出良好的效果。深度学习与外科专业知识的融合丰富了肠梗阻治疗的决策。所提出的模型可以帮助外科医生处理肠梗阻等复杂情况，并促进技术与临床智慧的协同作用，从而推动患者护理。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Medical Robotics and Computer Assisted Surgery 医学-外科

CiteScore

4.50

自引率

12.00%

发文量

131

审稿时长

6-12 weeks

期刊介绍： The International Journal of Medical Robotics and Computer Assisted Surgery provides a cross-disciplinary platform for presenting the latest developments in robotics and computer assisted technologies for medical applications. The journal publishes cutting-edge papers and expert reviews, complemented by commentaries, correspondence and conference highlights that stimulate discussion and exchange of ideas. Areas of interest include robotic surgery aids and systems, operative planning tools, medical imaging and visualisation, simulation and navigation, virtual reality, intuitive command and control systems, haptics and sensor technologies. In addition to research and surgical planning studies, the journal welcomes papers detailing clinical trials and applications of computer-assisted workflows and robotic systems in neurosurgery, urology, paediatric, orthopaedic, craniofacial, cardiovascular, thoraco-abdominal, musculoskeletal and visceral surgery. Articles providing critical analysis of clinical trials, assessment of the benefits and risks of the application of these technologies, commenting on ease of use, or addressing surgical education and training issues are also encouraged. The journal aims to foster a community that encompasses medical practitioners, researchers, and engineers and computer scientists developing robotic systems and computational tools in academic and commercial environments, with the intention of promoting and developing these exciting areas of medical technology.