Cardiac Model

Cardiac modeling aims to create accurate computational representations of the heart's structure and function, primarily to improve diagnosis, treatment planning, and drug development. Current research emphasizes developing personalized models using diverse data sources (ECG, PPG, CT, MRI, electroanatomical maps) and advanced machine learning techniques, including neural networks (e.g., transformers, graph neural networks, physics-informed neural networks), Bayesian frameworks, and optimization algorithms. These models are being used to improve the understanding of cardiac electrophysiology, mechanics, and the impact of disease and drugs, ultimately leading to more precise and effective interventions.