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) ...
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.
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