Abstract
Patient-specific computational fluid dynamics (CFD) simulations can provide invaluable insight into the interaction of left atrial appendage (LAA) morphology, hemodynamics, and the formation of thrombi in atrial fibrillation (AF) patients. Nonetheless, CFD solvers are notoriously time-consuming and computationally demanding, which has sparked an ever-growing body of literature aiming to develop surrogate models of fluid simulations based on neural networks. The present study aims at developing a deep learning (DL) framework capable of predicting the endothelial cell activation potential (ECAP), an in-silico index linked to the risk of thrombosis, typically derived from CFD simulations, solely from the patient-specific LAA morphology. To this end, a set of popular DL approaches were evaluated, including fully connected networks (FCN), convolutional neural networks (CNN), and geometric deep learning. While the latter directly operated over non-Euclidean domains, the FCN and CNN approaches required previous registration or 2D mapping of the input LAA mesh. First, the superior performance of the graph-based DL model was demonstrated in a dataset consisting of 256 synthetic and real LAA, where CFD simulations with simplified boundary conditions were run. Subsequently, the adaptability of the geometric DL model was further proven in a more realistic dataset of 114 cases, which included the complete patient-specific LA and CFD simulations with more complex boundary conditions. The resulting DL framework successfully predicted the overall distribution of the ECAP in both datasets, based solely on anatomical features, while reducing computational times by orders of magnitude compared to conventional CFD solvers.
Highlights
Atrial fibrillation (AF) is the most common clinically significant arrhythmia, with a cumulative lifetime development risk above 30% in individuals of European ancestry (Benjamin et al, 2019)
Non-valvular atrial fibrillation (AF) is responsible for 15–20% of all cardioembolic ischemic strokes, which preferentially form at the left atrial appendage (LAA) (Cresti et al, 2019), an heterogeneous, tubular structure derived from the anterior wall of the left atrium (LA)
The endothelial cell activation potential (ECAP) distributions resulting from both simulations were distinct due to the different geometry and boundary conditions
Summary
Atrial fibrillation (AF) is the most common clinically significant arrhythmia, with a cumulative lifetime development risk above 30% in individuals of European ancestry (Benjamin et al, 2019). Non-valvular AF is responsible for 15–20% of all cardioembolic ischemic strokes, which preferentially form at the left atrial appendage (LAA) (Cresti et al, 2019), an heterogeneous, tubular structure derived from the anterior wall of the LA. In this regard, researchers have explored the correlation between LAA morphology and the risk of stroke (Yaghi et al, 2020; Dudzinska-Szczerba et al, 2021; Słodowska et al, 2021). The results have been ambiguous, as the current classifications and associated morphological parameters of the LAA are often entirely subjective, hand-crafted features; there is a need for more systematic shape analysis of the LAA with advanced and observer-independent computational tools such as statistical atlases (Slipsager et al, 2019)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
