Digital Twinning for Personalized Atrial Fibrillation Care

The goal of the TwinCare-AF project is to develop innovative core methodologies for accurate and real-time calibration of cardiovascular electrophysiological models and to support medical decisions in the context of atrial fibrillation and catheter ablation therapy planning.

The proposed approach will focus on the generation of digital twins of patient hearts, calibrated through robust and efficient machine learning techniques, and able to replicate measured clinical data, such as electrocardiogram and electrogram recordings.

Specifically, physics-informed and/or deep-learning techniques will be extended and implemented within the context of anatomically-accurate and biophysically-detailed cardiac electrophysiology, to accelerate the solution of classical forward electrophysiological model, and to solve inverse problems for identifying patient-specific physical and tissue properties of the heart.

Additionally, a robust methodology for verification, validation, and uncertainty quantification will be adopted to showcase the agreement between model predictions and empirical observations, and to provide reliable estimates of confidence in the model predictions.

The developed approach will be used to predict atrial fibrillation progression and determine potential ablation sets for individual patients. The predictions of the developed model will undergo testing through in vivo intraoperative clinical measurements.

To enhance easy flow, robust analysis, and interpretation of patient-specific data, the novel real-time mathematical workflow for atrial fibrillation simulations will be integrated into a clinically viable platform.

These tasks will leverage leading-edge mathematical methodologies, improve the observation-to-diagnosis clinical process by efficiently handling patient-specific data, and support therapy planning, ultimately enabling a scalable translation to large population cohorts.