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急性尾炎中的人工智能:对机器学习和深度学习应用的全面审查

Sami Akbulut^1,2, Zeynep Kucukakcali³, Cemil Colak³

¹Surgery and Liver Transplantation, Inonu University Faculty of Medicine, Malatya 44280, Türkiye.

World journal of gastroenterology

|December 8, 2025

概括

通过分析临床和成像数据,人工智能 (AI) 显著改善了急性尾炎 (AAp) 诊断. 机器学习和深度学习模型提供高准确度,有助于区分复杂的与不复杂的案例.

科学领域:

医疗成像医学成像
人工智能的人工智能
临床诊断临床诊断临床诊断

背景情况:

急性尾炎 (AAp) 是一种常见的手术紧急情况,具有诊断挑战.
传统方法在灵敏度和特异性方面存在局限性,特别是在非典型的呈现方面.
人工智能,包括机器学习 (ML) 和深度学习 (DL),显示出增强AAp诊断的希望.

研究的目的:

审查当前的AI应用在诊断成人和儿科AAp.
用临床数据,放射性成像和决策支持系统来评估AI模型.
评估可解释AI (XAI) 对临床信任的影响.

主要方法:

在AAp诊断中对AI进行65项研究的系统审查.
分析ML模型 (例如随机森林,SVM) 和DL模型 (例如CNN).
评估整合多式联络数据 (临床,实验室,成像) 和XAI技术的AI.

主要成果:

ML和DL模型实现了AAp诊断的>90%的灵敏度和特异性.
DL模型,特别是CNN,在解释图像方面表现优于放射科医生.
多式人工智能模型展示了卓越的诊断精度.
XAI的方法提高了模型的透明度和临床医生的信任.

结论:

人工智能显著提高了AAp的诊断准确性和并发症差异化.
目前的证据在很大程度上是追溯的;前性,多中心研究是临床实施所需的.
将人工智能集成到紧急护理工作流程中是未来的方向.

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.复杂的尾炎是一个复杂的疾病.决策支持系统决策支持系统深度学习是一种深度学习.诊断诊断诊断诊断诊断可解释的人工智能机器学习是机器学习.预测建模的预测建模.

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