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基于深度学习的阻塞性冠状动脉疾病预测从心肌 perfusion SPECT SPECT

Yu Du^1,2,3, Bingjie Wang^1,4, Ching-Ni Lin⁵

¹Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macau SAR, China.

European journal of nuclear medicine and molecular imaging

|November 17, 2025

概括

深度学习 (DL) 模型使用心肌 perfusion SPECT (MP-SPECT) 扫描准确预测冠状动脉疾病 (CAD). 纳入临床因素显著提高了诊断准确度,为冠状动脉血管学提供了一个非侵入性的替代方案.

科学领域:

心血管成像 - 心血管成像
人工智能在医学中的应用
放射学和核医学放射学和核医学

背景情况:

冠状动脉疾病 (CAD) 诊断传统上依赖于侵入性冠状动脉血管学 (ICA).
心肌 perfusion SPECT (MP-SPECT) 是一种用于评估心脏功能的非侵入性成像技术.
需要改进的非侵入性方法来预测阻塞性CAD.

研究的目的:

应用深度学习 (DL) 技术来预测MP-SPECT的CAD诊断.
为了评估DL模型的性能,使用不同的数据输入和减弱校正方法.
评估结合临床因素对诊断准确性的影响.

主要方法:

对接受MP-SPECT的515名患者的回顾性分析.
为基于DL的减弱校正 (DLAC) 和CAD预测 (每个患者和每个血管) 开发DL模型.
训练DL模型具有各种输入:没有AC (NAC),DLAC,基于CT的AC (CTAC),仅有压力,压力/休息和压力+休息数据,有或没有临床因素.

主要成果:

与NAC相比,DLAC在数据集和分析中改善了AUC.
基于CTAC的压力+休息输入实现了每患者CAD预测的AUC为0.84.
结合临床因素 (性别,年龄,高血压) 进一步提高了AUC,每患者分析为0.92,每血管分析为0.80.

结论:

基于DL的减弱校正,CTAC,综合应力/休息数据和临床因素显著提高了MP-SPECT对CAD的预测性能.
DL模型为CAD诊断提供了一个有希望的非侵入性方法,可能减少对侵入性手术的需求.

关键词:

冠状动脉疾病是一种冠状动脉疾病.深度学习是一种深度学习.侵袭性冠状动脉血管学侵袭性冠状动脉血管学心肌 perfusion SPECT 的使用方法

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