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在AI胸部X射线算法用于肺癌检测的临床评估中的参考标准方法:系统性审查

Sean F Duncan¹, Andrew C Kidd², Jesus Perdomo Lampignano³

¹Digital Health Validation Lab, University of Glasgow, Imaging Centre of Excellence, Langlands Dr, Glasgow G51 4LB, UK; The Beatson West of Scotland Cancer Center, 1053 Great Western Rd, Glasgow G12 0YN, UK.

European journal of radiology

|September 17, 2025

概括

这次审查突出了人工智能 (AI) 对肺癌检测在胸部X射线 (CXRs) 上的评估方式的不一致性. 标准化参考标准对于可靠的AI临床实施至关重要.

科学领域:

放射学和医学成像学医学成像学
医疗保健中的人工智能
在瘤学瘤学.

背景情况:

肺癌是全球癌症死亡的主要原因,强调了早期检测的必要性.
人工智能 (AI) 显示了在解释胸部X射线 (CXRs) 对于胸部恶性瘤方面提高放射科医生的性能的潜力.
对CXR分析人工智能的临床采用需要标准化的患者级参考标准方法.

研究的目的:

系统地审查和描述用于对CXR肺癌检测AI算法的临床评估中使用的参考标准方法.

主要方法:

对多个数据库 (MEDLINE,Embase,CENTRAL) 和试验注册表进行了全面的搜索.
两名独立审查员选了研究,第三名审查员解决了分歧.
如果研究缺乏在现实世界队列中的外部验证,则被排除在外;偏差是使用修改后的QUADAS-2工具进行评估的.

主要成果:

在1679篇选论文中,包括46篇,评估了24个不同的AI解决方案.
在参考标准方法中发现了显著的异质性,包括目标异常,模式,专家小组和仲裁.
报告不足 (25%) 和偏见高风险 (66%) 在所有研究中普遍存在.

结论:

这是CXRAI分析胸部恶性瘤患者水平参考标准方法的第一个系统描述.
符合临床工作流程的标准化参考标准和遵守报告准则对于翻译进展至关重要.
一个值得注意的局限性是这个领域中前性研究的稀缺性.

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