The assessment of left atrial appendage (LAA) thrombogenesis has experienced major advances with the adoption of patient-specific computational fluid dynamics (CFD) simulations. Nonetheless, due to the vast computational resources and long execution times required by fluid dynamics solvers, there is an ever-growing body of work aiming to develop surrogate models of fluid flow simulations based on neural networks. The present study builds on this foundation by developing a deep learning (DL) framework capable of predicting the endothelial cell activation potential (ECAP), linked to the risk of thrombosis, solely from the patient-specific LAA geometry. To this end, we leveraged recent advancements in Geometric DL, which seamlessly extend the unparalleled potential of convolutional neural networks (CNN), to non-Euclidean data such as meshes. The model was trained with a dataset combining 202 synthetic and 54 real LAA, predicting the ECAP distributions instantaneously, with an average mean absolute error of 0.563. Moreover, the resulting framework manages to predict the anatomical features related to higher ECAP values even when trained exclusively on synthetic cases.
翻译:在对左侧附加物(LAA)血栓形成进行评估后,在采用特定病人的计算流体动态(CFD)模拟方法后,在评估左侧附加物(LAA)血栓形成方面取得了重大进展,然而,由于流体动态求解器所需的大量计算资源和长时间执行时间,越来越多的工作旨在开发基于神经网络的液体流动模拟替代模型;本研究以这一基础为基础,开发了能够预测内侧细胞激活潜力(ECAP)的深学习(DL)框架,仅与特定病人的LAAA几何性血栓化风险(ECAP)相关联。为此,我们利用了几何DL的近期进展,这无缝地扩大了脉动神经网络(CNN)的无与伦比的潜能,将Memshes等非欧洲光谱系数据扩展为无缝,该模型的数据集结合了202个合成和54个真实的LAAAA,即时预测ECAP的分布,平均绝对误差0.563。此外,由此形成的框架得以预测了与高级ECAP值相关的解剖剖面特征特征,即使完全只用于合成案例的培训。