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一个个性化的计算框架用于诊断心脏 perfusion 缺陷

Elisabetta Criseo¹, Andrea Baggiano², Giovanni Montino Pelagi³

¹Cardio-Tech Lab, Centro Cardiologico Monzino, Via Carlo Parea 4, Milano, 20138, Italy; Dipartimento di Elettronica Informazione e Bioingeneria, Politecnico di Milano, Via Ponzio 34/5, Milano, 20133, Italy.

Computer methods and programs in biomedicine

|August 13, 2025

概括

这项研究引入了一个AI框架,可以从常规CT扫描中预测心肌动脉血流 (MBF),从而改善冠状动脉疾病的诊断. 该模型准确地识别了健康和病态的 perfusion 区域.

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
计算生理学计算生理学

背景情况:

心肌血流 (MBF) 对于评估心肌输液至关重要.
目前的方法通常需要额外的临床测试,如动态CT输液.
需要使用非侵入性方法来预测MBF.

研究的目的:

开发和验证用于预测MBF的计算框架.
为了提高冠状动脉疾病的诊断,使用常规CT数据.
将人工智能与计算机建模集成为 perfusion 评估.

主要方法:

人工智能驱动的冠状动脉和心肌几何结构的重建.
一个具有3D冠状动脉和三隔膜心肌模型的计算模型.
使用6名患者的数据进行盲目校准.

主要成果:

在28名患者的实验和临床测量中验证了MBF预测.
在对健康与病态 perfusion 区域进行分类时的高精度 (回忆值 0.81,精度 0.68,精度 0.7).
人工智能预测的MBF与既定指标的一致性.

结论:

本研究介绍了使用这种AI框架对MBF的首次大规模预测和验证.
开发的计算框架显示了未来心脏病学临床应用的前景.
这些发现支持AI在常规CT中的潜力,以改善心脏诊断.

关键词:

计算流体动力学的流体动力学.冠状动脉疾病是一种冠状动脉疾病.心肌动脉的血液流动是如何进行的

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