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Updated: Jul 4, 2025

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深度学习模型与放射科医生在COVID-19肺炎中的诊断性能:系统性审查和元分析

Mohammadreza Chavoshi¹, Sara Zamani², Seyed Ali Mirshahvalad³

¹Department of Radiology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran.

Clinical imaging

|February 1, 2024

概括

与放射科医生相比,深度学习 (DL) 模型在胸部CT扫描上检测COVID-19肺炎的灵敏度更高. 这些先进的AI工具显示出在临床环境中增强放射科医生的性能的潜力.

科学领域:

放射学放射学是一门学科.
人工智能的人工智能
医疗成像医学成像

背景情况:

胸部计算机断层扫描 (CT) 对于诊断COVID-19肺炎至关重要.
之前的研究已经将深度学习 (DL) 模型与放射科医生进行了比较,以在CT上检测COVID-19.
缺乏对这些比较研究的全面评估.

研究的目的:

进行对原始文章进行元分析,比较DL模型和放射科医生在胸部CT上检测COVID-19肺炎.
为了集体评估DL模型与人类专家的诊断性能.

主要方法:

在Scopus,科学网络和PubMed上进行系统的文献搜索,直到2023年2月1日.
包括原始科学文章比较DL模型训练COVID-19肺炎检测CT与放射科医生.
进行元分析以确定聚合的灵敏度和特异性,并考虑异质性.

主要成果:

22篇文章符合元分析的纳入标准.
在DL模型中,比放射科医生 (0.829,p < 0.001) 显著更高的聚合灵敏度 (0.933).
在DL模型 (0.905) 和放射科医生 (0.897,p = 0.746) 之间,聚合特异性是相似的.
在区分COVID-19和社区获得的肺炎方面,DL模型显示出显著更高的灵敏度 (0.915对0.836,p=0.001).

结论:

深度学习模型在胸部CT上的COVID-19肺炎查方面取得了高性能.
在临床实践中,DL模型展示了作为一种工具来增强放射科医生的能力的潜力.
这些发现支持将AI整合到放射学工作流程中,以提高诊断准确度.

关键词:

人工智能的人工智能是人工智能.在 COVID-19 疫情中,深度学习是一种深度学习.进行元分析分析.肺炎是一种肺炎.

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