A Novel Deep Learning Model for Patient-Specific Computational Modeling of Cardiac Electrophysiology
DescriptionPatient-specific computational modeling of cardiac electrophysiology (EP) has been shown to have a potential utility in a wide range of arrhythmia prognostics and treatment applications. Yet, it requires extensive computational resources, posing a critical challenge for expanding its application in the clinic. Hence, there is a pressing need to develop accurate and trustable personalized EP heart models with affordable computational costs. We develop a novel approach to modeling patient-specific cardiac electrophysiology, which utilizes, at a much-reduced cost, deep learning in lieu of solving the equations for electrical wave propagation. The new approach is developed based on an operator-learning neural network (DeepONet) to predict the propagation of electric signals in different patient-specific heart chambers. We train our model to predict electric signal propagation given the encoded geometric information with the pacing location. After training, the accuracy of the deep learning approach is demonstrated by comparing its predictions with simulation results on unseen geometries. We propose a novel deep-learning approach for modeling patient-specific cardiac electrophysiology with high accuracy and reduced computational cost. This development paves the way for utilization of personalized cardiac EP modeling in clinical applications, as part of the clinical workflow, in ablation target predictions and arrhythmia morphology assessment.
TimeTuesday, June 2717:30 - 18:00 CEST