Abstract

Cardiac fibers play an essential role in the electrical and mechanical function of the heart, making them a critical parameter in cardiac modeling. However, identifying the 3D arrangement of the fibers in clinical practice still encounters some limitations. Solving the inverse problem of inferring the fiber orientations from electrophysiological data has been reported as a potential method for identifying patient-specific fiber orientations. In this work, we employ ?-Fibernet, a recently proposed Physics-Informed Neural Network (PINN) model, to reconstruct the fiber orientations on the right ventricle’s endocardial surface from a single activation map. The model equips the traditional PINN framework with an ensemble of parallel neural networks to cope with the uncertainty in the fiber approximations. Each ensemble member estimates the activation times and fiber orientations. The best fiber orientations are finally selected using a specific method to reduce the uncertainty of predictions. We evaluated the performance of the model in a simple propagation pattern and a more realistic propagation pattern incorporating the influence of the Purkinje network. Our results indicate the robustness of ?-Fibernet and its capacity to learn complex activation patterns and fiber distributions while trained with only a single activation map. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.