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 ...
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.
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