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在非对比CT中检测缺血性中风的人工智能:系统性审查和元分析

Wenzhuo Shen¹, Jihong Peng², Jie Lu¹

¹Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing 100053, China (W.S., J.L.); Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing 100053, China (W.S., J.L.).

Academic radiology

|September 26, 2025

概括

人工智能 (AI) 在内部验证期间在非对比CT (NCCT) 上检测缺血性中风 (IS) 中表现出可接受的性能. 然而,有限的外部验证限制了其现实世界的临床适用性.

科学领域:

医疗成像医学成像
人工智能的人工智能
神经学神经学

背景情况:

缺血性中风 (IS) 的诊断依赖于非对比CT (NCCT).
人工智能 (AI) 模型是医疗图像分析的新兴工具.
在NCCT中评估AI对IS的诊断准确度至关重要.

研究的目的:

系统地审查和元分析AI模型在NCCT中用于IS检测的诊断准确性.
将AI模型的诊断性能与临床医生的诊断性能进行比较.
评估人工智能协助对临床医生的绩效的影响.

主要方法:

在主要数据库 (PubMed,科学网,Cochrane,IEEE Xplore,Embase) 进行系统的文献搜索,直到2025年2月.
包括使用人工智能对人类NCCT图像进行IS检测/分类的研究.
使用PROBAST评估偏差风险;元分析采用聚合的灵敏度,特异性和HSROC曲线.

主要成果:

在内部验证中,人工智能显示了91.2%的综合灵敏度和96.0%的特异性 (32项研究中的74项试验).
外部验证显示AI敏感性较低 (59.8%),但特异性高 (97.3%).
在人工智能协助下,临床医生的表现得到了改善 (灵敏度为83.7%,特异性为86.7%).
与数据增强和转移学习技术相关的更高的人工智能敏感性.
发现了显著的异质性和高偏差风险 (58%的研究).

结论:

在NCCT内部,AI在IS检测方面表现出可接受的诊断性能.
显著的异质性影响了元分析结果.
有限的外部验证和通用性阻碍了AI的立即现实世界临床应用.

关键词:

人工智能的人工智能是人工智能.深度学习是一种深度学习.缺血性中风是因为缺血性中风.进行元分析分析.没有对比度的CTCT.

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