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Digital Twinning for Personalized Atrial Fibrillation Care

CORDIS · observation · Publication date unknown

e 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 indi

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recordType

award

status

SIGNED

region

EU

value

183600.96

unit

EUR