人工智能在诊断肺栓塞中的作用:系统性审查和元分析
Alireza Farzaei1,2, Fateme Hajzeinolabedini3, Babak Sharif Kashani4
1AJA university of medical sciences, school of medicine, Tehran, Iran.
Archives of academic emergency medicine
|February 10, 2026
概括
人工智能 (AI) 模型在各种成像技术中在诊断肺栓塞 (PE) 中表现出高准确性. 虽然有效,但在外部验证中,AI性能略有下降,这表明潜在的通用性限制.
科学领域:
- 医学成像分析 医学成像分析
- 医疗保健中的人工智能
- 诊断准确性研究 诊断准确性研究
背景情况:
- 肺栓塞 (PE) 的延迟诊断导致患者的发病率,死亡率和长期住院时间的增加.
- 准确和及时的PE诊断对于有效的患者管理和改善结果至关重要.
- 人工智能 (AI) 在提高PE诊断能力方面的潜力需要彻底评估.
研究的目的:
- 系统地评估AI模型在检测肺栓塞 (PE) 中的诊断准确度,使用各种成像方式.
- 综合来自多项研究的证据,为PE诊断提供AI性能指标的综合估计.
主要方法:
- 在主要数据库 (PubMed/MEDLINE,Scopus,Embase,Web of Science) 进行了全面的系统文献搜索,截至2025年1月.
- 包含的研究使用QUADAS-2工具对偏差风险进行了评估,数据由两个独立审稿人提取.
- 随机效应元分析被用来计算聚合的灵敏度,特异性,概率比率,诊断几率比率 (DOR) 和ROC曲线下的面积 (AUC).
主要成果:
- 该元分析包括28项涉及超过46,900名参与者的研究.
- 内部验证显示AI性能优异,聚合灵敏度为0.91和特异性为0.94 (AUC=0.95).
- 外部验证结果略低,但仍然具有高性能指标 (灵敏度=0.89,特异性=0.88,AUC=0.94),并注意到显著的异质性.
结论:
- 人工智能模型表明,在各种成像方式中,对精确的PE检测有很大的潜力.
- 从内部验证到外部验证的性能下降表明AI模型通用性的局限性.
- 研究中的大量异质性需要仔细解释聚合的诊断准确性估计.
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