Learning atrial fiber orientations and conductivity tensors from intracardiac maps using physics-informed neural networks
This work addresses a specific challenge in cardiac electrophysiology for diagnosing atrial fibrillation, representing an incremental step in extracting more information from existing clinical data.
The authors tackled the problem of inferring atrial fiber orientations from electroanatomical maps using physics-informed neural networks, achieving an RMSE of 2.2ms on synthetic data and outperforming a state-of-the-art method on patient data.
Electroanatomical maps are a key tool in the diagnosis and treatment of atrial fibrillation. Current approaches focus on the activation times recorded. However, more information can be extracted from the available data. The fibers in cardiac tissue conduct the electrical wave faster, and their direction could be inferred from activation times. In this work, we employ a recently developed approach, called physics informed neural networks, to learn the fiber orientations from electroanatomical maps, taking into account the physics of the electrical wave propagation. In particular, we train the neural network to weakly satisfy the anisotropic eikonal equation and to predict the measured activation times. We use a local basis for the anisotropic conductivity tensor, which encodes the fiber orientation. The methodology is tested both in a synthetic example and for patient data. Our approach shows good agreement in both cases, with an RMSE of 2.2ms on the in-silico data and outperforming a state of the art method on the patient data. The results show a first step towards learning the fiber orientations from electroanatomical maps with physics-informed neural networks.