Digital twinning of cardiac electrophysiology models from the surface ECG: a geodesic backpropagation approach
This addresses the problem of non-invasive, personalized cardiac modeling for clinical applications, though it appears incremental as it builds on existing eikonal-based approaches.
The study tackled the challenge of building patient-specific cardiac electrophysiology models from surface ECG data by introducing Geodesic-BP, a novel method that optimizes eikonal equation parameters to match ECGs, achieving high accuracy in synthetic tests and showing promise on a rabbit model dataset.
The eikonal equation has become an indispensable tool for modeling cardiac electrical activation accurately and efficiently. In principle, by matching clinically recorded and eikonal-based electrocardiograms (ECGs), it is possible to build patient-specific models of cardiac electrophysiology in a purely non-invasive manner. Nonetheless, the fitting procedure remains a challenging task. The present study introduces a novel method, Geodesic-BP, to solve the inverse eikonal problem. Geodesic-BP is well-suited for GPU-accelerated machine learning frameworks, allowing us to optimize the parameters of the eikonal equation to reproduce a given ECG. We show that Geodesic-BP can reconstruct a simulated cardiac activation with high accuracy in a synthetic test case, even in the presence of modeling inaccuracies. Furthermore, we apply our algorithm to a publicly available dataset of a biventricular rabbit model, with promising results. Given the future shift towards personalized medicine, Geodesic-BP has the potential to help in future functionalizations of cardiac models meeting clinical time constraints while maintaining the physiological accuracy of state-of-the-art cardiac models.