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- Title
Whole-heart electromechanical simulations using Latent Neural Ordinary Differential Equations.
- Authors
Salvador, Matteo; Strocchi, Marina; Regazzoni, Francesco; Augustin, Christoph M.; Dede', Luca; Niederer, Steven A.; Quarteroni, Alfio
- Abstract
Cardiac digital twins provide a physics and physiology informed framework to deliver personalized medicine. However, high-fidelity multi-scale cardiac models remain a barrier to adoption due to their extensive computational costs. Artificial Intelligence-based methods can make the creation of fast and accurate whole-heart digital twins feasible. We use Latent Neural Ordinary Differential Equations (LNODEs) to learn the pressure-volume dynamics of a heart failure patient. Our surrogate model is trained from 400 simulations while accounting for 43 parameters describing cell-to-organ cardiac electromechanics and cardiovascular hemodynamics. LNODEs provide a compact representation of the 3D-0D model in a latent space by means of an Artificial Neural Network that retains only 3 hidden layers with 13 neurons per layer and allows for numerical simulations of cardiac function on a single processor. We employ LNODEs to perform global sensitivity analysis and parameter estimation with uncertainty quantification in 3 hours of computations, still on a single processor.
- Subjects
HEART physiology; COMPUTER simulation; BIOLOGICAL models; CARDIOVASCULAR diseases; ARTIFICIAL intelligence; HEART failure; HEMODYNAMICS; ARTIFICIAL neural networks; MATHEMATICAL models; THEORY; CARDIAC pacing; ELECTROPHYSIOLOGY; ALGORITHMS; HEART ventricles
- Publication
NPJ Digital Medicine, 2024, Vol 7, Issue 1, p1
- ISSN
2398-6352
- Publication type
Article
- DOI
10.1038/s41746-024-01084-x