在冠状动脉扫描中使用序列对比学习识别急性冠状动脉综合征的发病原因
Xiaozhi Ma1, Yusaku Shibata2, Osamu Kurihara2
1School of Information, Kochi University of Technology, Kami 782-8502, Kochi, Japan.
The American journal of cardiology
|September 3, 2025
概括
这项研究引入了序列对比学习 (SeqCon) 来区分急性冠状动脉综合征 (ACS) 中的斑块破裂和斑块侵蚀,仅使用冠状动脉血管学. 这种人工智能方法有望在没有侵入性成像的情况下引导治疗决策.
科学领域:
- 心脏病学
- 人工智能
- 医学成像
背景情况:
- 急性冠状动脉综合征 (ACS) 的诊断依赖于识别底层机制,如斑块破裂 (PR) 和斑块侵蚀 (PE).
- 区分PR与PE对于定制治疗至关重要,PR通常通过支架进行管理,PE可能以保守的方式进行管理.
- 目前的方法通常需要侵入性冠状动脉成像,因此需要使用非侵入性诊断工具.
研究的目的:
- 开发和评估一个神经网络模型,序列对比学习 (SeqCon),仅使用冠状动脉血管学 (CAG) 来区分PR和PE.
- 通过CAG分析评估非侵入性诊断ACS病变的可行性.
主要方法:
- 使用了278名ACS患者 (172名PR,106名PE) 的842个视频,通过光学一致性断层扫描 (OCT) 证实了基底真相.
- 开发了SeqCon,一种新的对比学习方法,以增强连续视频的特征学习,以改善分类.
- 在一个由18名PR和11名PE患者组成的外部测试组中验证了该模型.
主要成果:
- SeqCon在患者水平上取得了很高的性能:准确度为82. 8%,敏感度为88. 9%,特异性为72. 3%,预测值为84. 2%和预测值为80. 0%.
- 证明该模型只使用CAG数据有效区分PR和PE.
- 这标志着对比学习首次通过CAG诊断ACS机制.
结论:
- 用SeqCon等先进人工智能分析的冠状动脉图可以在ACS中区分斑块破裂和斑块侵蚀.
- 这种非侵入性方法有可能指导临床管理决策,从而减少对更侵入性诊断程序的需求.
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