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基于深度学习,从非对比计算机断层扫描图像中量化心表脂肪组织体积:一项多中心研究

Shuang Leng^1,2, Nicholas Cheng³, Eddy Tan³

¹CVS.AI, National Heart Research Institute Singapore, National Heart Centre Singapore, 5 Hospital Drive, Singapore 169609, Singapore.

European heart journal. Digital health

|November 21, 2025

概括

一个深度学习系统可以在30秒以下的时间内从CT扫描中精确量化心上脂肪组织 (EAT) 的体积. 这种自动化的EAT评估有助于预测不同人群冠状动脉疾病 (CAD) 风险.

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:

副心脏脂肪组织 (EAT) 是冠状动脉疾病 (CAD) 进展的关键生物标志物.
准确量化EAT体积对于风险分层至关重要.
目前用于EAT体积测量的方法可能耗时.

研究的目的:

开发和验证用于自动化EAT体积量化深度学习系统.
用非对比计算断层扫描 (NCCT) 来评估系统的性能.
评估该系统在不同族裔群体中的通用性.

主要方法:

在1243个NCCT扫描中训练了一个3DUNet++深度学习模型,用于心周细分.
应用了强度值来得出EAT体积.
该模型在160名患者的外部队列上得到验证,其中包括非亚洲人.

主要成果:

人工智能系统在每次扫描大约30秒内量化了EAT的数量.
在AI预测和专家注释的EAT体积之间观察到很好的一致性 (r = 0.975).
人工智能衍生出的EAT体积独立预测了阻塞性CAD,并且在加入冠状动脉评分时改善了风险预测.

结论:

一个经过验证的深度学习系统可以从NCCT扫描中实现自动化的EAT体积量化.
该系统在不同种族中显示出高准确性和通用性.
这种工具有可能用于常规的EAT评估和增强的CAD风险分层.

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.胸前脂肪组织的副心脏脂肪组织.没有对比度的CTCT.

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