人工智能用于检测胸部X射线图上的大动脉形化:一个系统的审查.
Krzysztof Żerdziński1,2, Julita Janiec1,2, Maja Dreger1,3
1Student Scientific Association of Computer Analysis and Artificial Intelligence, Department of Radiology and Nuclear Medicine, Medical University of Silesia, 40-752 Katowice, Poland.
Diagnostics (Basel, Switzerland)
|January 28, 2026
概括
人工智能 (AI) 模型可以在胸部X射线 (CXR) 上有效检测大动脉形化 (AAC),用于心血管风险评估. 然而,人工智能模型设计和验证的变化需要标准化,以便广泛的临床使用.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 心血管疾病 心血管疾病
背景情况:
- 胸部X射线图 (CXRs) 上的大动脉形化 (AAC) 是一个重要的心血管事件预测器.
- 在例行放射学评估中,AAC经常被忽视.
- 人工智能 (AI) 提供了自动化AAC检测的潜力.
研究的目的:
- 系统地审查AI模型的诊断准确性,以检测CXR上的AAC.
- 评估AI驱动的AAC检测的临床实施潜力.
主要方法:
- 按照PRISMA 2020指南进行系统审查.
- 搜索了Embase,PubMed,Scopus和科学网络 (2020年1月至2025年10月) 的数据.
- 包括在大型数据集上使用卷积神经网络 (CNN) 的回顾性研究;偏差用QUADAS-2进行评估.
主要成果:
- 分析了三项研究 (2022-2024) 涉及约270万张图像.
- 人工智能模型显示出高的诊断歧视 (AUROC 0.81-0.99),在外部队列中性能有所不同.
- 观察到显著的灵敏度-特异性权衡;由于异质性和缺乏截面成像标准,GRADE确定性很低.
结论:
- 深度学习模型显示出可靠的AAC检测CXR的前景,使得机会性心血管风险分层成为可能.
- 人工智能模型架构和验证中的异质性阻碍了广泛的可比性.
- 标准化的注释和外部验证对于人工智能工具的临床通用性至关重要.
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