Deep learning surrogate of computational fluid dynamics for thrombus formation risk in the left atrial appendage

Abstract

Atrial fibrillation (AF) is the most common clinically significant arrhythmia, often severely disrupting cardiac hemodynamics and drastically increasing the risk of thromboembolic events. Around 90% of such intracardiac thrombus formation in AF patients takes place in the left atrial appendage (LAA). Such thrombus have been related to blood stasis, which at the moment, can be only assessed through noisy imaging data from transesophageal echocardiography (TEE) at one single point in space and time, vastly oversimplifying the characterization of the complex 4D nature of blood flow patterns. Alternatively, attempts have been made to relate LAA morphology to the risk of thrombi formation, some studies suggesting reduced risk of thrombosis on chicken-wing morphologies. Nonetheless, such classification of the LAA morphology has been found to be highly inconsistent and subjective, excluding as well, several fundamental morphological parameters such as the ostium size or the pulmonary vein (PV) orientation among others. More recently, computational fluid dynamics (CFD) have been employed on the left atrium (LA), seeking to assess the risk of thrombogenesis more quantitatively. CFD has proven to be an invaluable tool in establishing a mechanistic relation between patient-specific organ morphology and its characteristic hemodynamics. In fact, it has long been implemented in other human tissues, such as the coronary arteries, cerebral aneurysms and the aorta with unparalleled success, enabling early diagnosis and risk assessment of various cardiovascular diseases. Nevertheless, traditional CFD methods are renowned for their large memory requirements and long computing times, which severely hinders its suitability for time-sensitive clinical applications. Hence, this thesis seeks to harness the immense potential of deep learning (DL) by developing a deep neural network (DNN), with the objective of generating a fast and accurate surrogate of CFD, capable of instantaneously evaluating the risk of thrombus formation in the LAA. Already having revolutionized fields such as data processing, it has only recently begun to employ DNNs in high-dimensional, complex dynamical systems such as fluid dynamics. In fact to our knowledge, this study represents the first successful implementation of a DL surrogate of CFD analysis in a structure as complex as the LAA, which had only been previously attempted in the aorta. For that purpose, two DL architectures have been successfully designed and trained, which receive the specific LAA geometry as an input, and accurately predict its corresponding endothelial cell activation potential (ECAP) map, parameter linked to the risk of thrombosis. The first approach, is based on a simple fully-connected feedforward network, while the latter, also embeds unsupervised learning. An statistical shape model (SSM) of the LAA was created to generate the training dataset, encompassing 210 virtual shapes, on which CFD simulations were performed to attain the ground truth ECAP mappings. Once trained, the final DL networks have accurately predicted the ECAP distributions resulting in an average error of 4.72% for the simple fully-connected network and 5.75% for the unsupervised learning model. Most importantly, the obtention of the ECAP predictions was quasi-instantaneous, orders of magnitude faster than conventional CFD. Therefore, this study is one of the first to demonstrate, the feasibility and unparalleled potential of DL models as accurate and substantially faster surrogates of CFD, potentially enabling future real-time assessment of thrombogenesis risk on the LAA.