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一个基于多式联络数据的冠状动脉狭窄严重程度的非侵入性预测模型

Jiyu Zhang¹, Jiatuo Xu¹, Liping Tu¹

¹College of Traditional Chinese Medicine, Shanghai University of Traditional Chinese medicine, Shanghai, China.

Frontiers in physiology

|June 18, 2025

概括

这项研究引入了一种新的非侵入性模型,使用多模式数据来评估冠状动脉疾病 (CAD) 的严重程度. 基于变压器的方法提供了一个可靠的替代入侵血管学精确风险分层的可靠替代方案.

科学领域:

生物医学工程生物医学工程
人工智能在医学中的应用
心血管诊断心血管诊断服务

背景情况:

目前冠状动脉疾病 (CAD) 诊断依赖于侵入性冠状动脉血管学,这带有程序性风险.
精确评估狭窄症的严重程度对于有效的CAD管理和治疗计划至关重要.
需要可靠的,非侵入性的方法来诊断和分层CAD风险.

研究的目的:

开发基于变压器的多式预测模型,用于对冠状动脉狭窄症严重性的非侵入性评估.
整合异质生物标志物,包括面部形态测量,心血管波形和生化指标,用于精确的风险分层.
为增强CAD诊断建立一个可解释的框架.

主要方法:

采用了基于变压器的架构,包括剩余模块和自适应加权.
多模式数据 (面部特征,嘴唇/舌头图像,脉冲/压力波,实验室指标) 从488名CAD患者中收集.
该模型经过内部和外部数据集的训练和验证,以预测狭窄症的严重程度.

主要成果:

该模型在训练数据集上评估冠状动脉狭窄风险时达到90%以上的准确性.
外部验证在真实世界的验证集上证明了85%的准确性.
多式联运数据和先进架构的整合显著改善了预测性能.

结论:

成功开发了一种基于变压器的多式模式,用于评估CAD狭窄症的严重程度.
该模型为侵入性诊断程序提供了一个临床上可行的替代方案.
多模式数据集成具有改善CAD诊断和患者护理的巨大潜力.

关键词:

评估心血管风险评估冠状动脉疾病是一种冠状动脉疾病.深度学习方法深度学习方法.针对疾病风险分层的机器学习多式联络预测多式联络预测

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