Cardiac Digital Twin

Cardiac digital twins (CDTs) are personalized, virtual representations of the heart used to understand and predict cardiac function. Current research focuses on improving the accuracy and efficiency of CDTs by integrating diverse data sources (e.g., ECG, MRI) and employing advanced computational methods, including deep learning models (like variational autoencoders and neural ordinary differential equations) and physics-based approaches, to solve the inverse electrocardiography problem and infer patient-specific electrophysiological parameters. This work aims to enhance the precision of diagnoses, personalize treatment planning (e.g., for myocardial infarction), and accelerate the development of new therapies by providing a powerful tool for simulating cardiac behavior under various conditions.