Deep Learning Framework for Real-Time Estimation of in-silico Thrombotic Risk Indices in the Left Atrial Appendage

Deep Learning Framework for Real-Time Estimation of in-silico Thrombotic Risk Indices in the Left Atrial Appendage

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...

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Bibliographic Details
Main Authors: Xabier Morales Ferez, Jordi Mill, Kristine Aavild Juhl, Cesar Acebes, Xavier Iriart, Benoit Legghe, Hubert Cochet, Ole De Backer, Rasmus R. Paulsen, Oscar Camara
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-06-01
Series:Frontiers in Physiology
Subjects:
geometric deep learning
left atrial appendage
convolutional neural network
thrombus-atrial fibrillation
computational fluid dynamics
principal component analysis
Online Access:https://www.frontiersin.org/articles/10.3389/fphys.2021.694945/full

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https://www.frontiersin.org/articles/10.3389/fphys.2021.694945/full

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