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.

本研究通过开发深度学习 (DL) 框架，仅根据患者的特定左心房附属物（LAA）几何形状来预测与血栓形成风险相关的内皮细胞活化潜力（ECAP）分布，建立在几何 DL 的最新进展基础上，该模型在综合合成和 54 个真实的 LAA 数据集上进行训练，平均平均绝对误差为 0.563。