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可解释和可重现的机器学习模型用于冠状动脉化和段级狭窄症分层在计算机断层扫描血管学上

Jian Chen¹, Hongqiu Wang², Yiran Wei³

¹Department of Radiology, School of Clinical Medicine, University of Cambridge, Cambridge, UK.

|November 27, 2025

概括

分析冠状动脉计算机断层扫描血管造影 (CCTA) 的机器学习模型可以使用稳定的成像功能准确量化冠状动脉疾病 (CAD). 这种方法在改善临床实践中的定量CAD评估方面具有前景.

科学领域:

心脏病学心脏病学
医疗成像医学成像
人工智能的人工智能

背景情况:

冠状动脉计算机断层扫描血管造影 (CCTA) 是诊断和管理冠状动脉疾病 (CAD) 的主要工具.
机器学习 (ML) 提供了使用CCTA数据进行定量CAD评估的潜力.

研究的目的:

评估来自CCTA的稳定成像特征,用于定量CAD评估.
开发和验证可解释的ML模型来量化冠状动脉化和狭窄症.

主要方法:

从SCOT-HEART试验中对909名参与者的后期分析.
在21个处理设置中评估CCTA衍生成像功能.
开发和验证可解释的ML模型 (SVM,KNN,MLP,Naïve Bayes,梯度增强,LightGBM) 以量化主要冠状动脉段的化和狭窄.

主要成果:

确定了549个稳定的成像特征.
最好的ML模型实现了84.2%的准确性和0.973的AUC预测冠状动脉化和狭窄.
在所有细分上,狭窄的分层精度超过了84.8%.

结论:

稳定的成像功能作为未来基于ML的定量冠状动脉评估的参考.
可解释的ML模型在量化冠状动脉化和段级狭窄症方面表现强.

关键词:

化量化量化的化.计算机断层扫描血管图谱.可解释的机器学习模型主要的冠状动脉部分.稳定的特征是稳定的特征.狭窄的评估狭窄的评估

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