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用基于机器学习的计算机断层扫描诊断急性尾炎:诊断可靠性和临床管理中的作用

Osman Sibic¹, Erkan Somuncu², Serhan Yilmaz³

¹General Surgery Service, Derik State Hospital, Derik, Turkey.

Journal of laparoendoscopic & advanced surgical techniques. Part A

|February 19, 2025

概括

人工智能 (AI) 在使用CT扫描诊断急性尾炎 (AA) 中表现有前途. 人工智能模型,特别是MobileNet v2,在识别AA方面表现出高准确度和灵敏度,有助于早期检测和预防并发症.

科学领域:

医疗成像医学成像
人工智能的人工智能
手术紧急情况手术紧急情况

背景情况:

急性尾炎 (AA) 是一种普遍的手术紧急情况,需要及时诊断以减轻严重后果.
目前AA的诊断方法包括体检,实验室检查和超声波和计算机断层扫描 (CT) 等成像.

研究的目的:

评估人工智能 (AI) 在分析CT图像以早期检测AA的有效性.
通过提高诊断准确度,探索AI在预防AA相关并发症方面的潜力.

主要方法:

分析了2019年1月至2023年6月期间被诊断患有AA的患者的1200张CT图像.
评估四种不同的AI模型,通过混矩阵和接收器操作特征分析确定性能指标.

主要成果:

移动网络v2获得了最高的准确度 (0.7908) 和精度 (0.8203),而Inception v3获得了最高的F分数 (0.7928).
在接收器操作特征分析中,MobileNet v2显示了0.8767的曲线下的显著区域 (AUC).
该研究报告了AA诊断中AI模型的最大灵敏度为77%和特异性为86%.

结论:

利用人工智能对CT图像的分析可以显著提高急性尾炎诊断的准确性.
将AI系统与CT成像集成为早期AA检测和管理提供了有价值的工具.
这些发现支持人工智能在临床实践中的作用不断扩大,特别是在诊断外科紧急情况时.

关键词:

急性尾炎是什么情况人工智能的人工智能是人工智能.计算机断层扫描 (CT) 是一种计算机断层扫描.诊断成像诊断成像的使用.

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