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人工智能模型在手腕道诊断中的准确性:系统性审查和元分析
John Milkovich1, Chloe R Wong2, Sumeet Sekhon3
1Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.
Plastic and reconstructive surgery
|February 2, 2026
概括
人工智能 (AI) 在诊断手腕道综合征 (CTS) 中表现出高准确度. 这一系统性审查发现,人工智能模型可靠地提高了CTS诊断效率和临床环境中的工作流程.
科学领域:
- 神经学 神经学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 手腕道综合征 (CTS) 诊断可以通过人工智能 (AI) 改进.
- 人工智能集成到神经传导研究 (NCS) 和超声波 (US) 中,提高了手管道 (IMNCT) 异形中枢神经病症的诊断性能.
- 人工智能有可能简化CTS诊断,提高可访问性和效率.
研究的目的:
- 系统地审查和评估CTS的AI模型的诊断准确性.
- 评估AI在使用已确定的诊断方式检测CTS方面的表现.
主要方法:
- 一个遵循PRISMA指南的系统审查,在PROSPERO注册 (CRD42024606291).
- 在Ovid MEDLINE,Ovid EMBASE和Cochrane CENTRAL进行的搜索涉及CTS诊断中人工智能的研究.
- 双变量分析用于计算聚合的灵敏度,特异性,AUC和概率比率 (LRs),用US,NCS和/或临床诊断作为参考标准.
主要成果:
- 包括20项研究,17项评估了CTS的AI诊断准确性.
- 人工智能模型显示出高的诊断性能:灵敏度为0.884,特异性为0.892,AUC为0.936,LR+为8.260,LR-0.131.
- 在一半的研究中,超声波 (美国) 是参考标准;NCS 在30%中使用. 总体而言,证据的确定性是中等的.
结论:
- 人工智能模型对可靠的CTS诊断有显著的前景.
- 人工智能可以作为一种有价值的临床辅助,提高诊断效率和标准化实践.
- 这些发现支持AI在改善CTS诊断工作流程和可访问性方面的作用.
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