Selected Publication:

SHR Neuro Cancer Cardio Lipid Metab Microb

Salvador, M; Strocchi, M; Regazzoni, F; Augustin, CM; Dede', L; Niederer, SA; Quarteroni, A.
Whole-heart electromechanical simulations using Latent Neural Ordinary Differential Equations.
NPJ Digit Med. 2024; 7(1): 90 Doi: 10.1038/s41746-024-01084-x [OPEN ACCESS]
Web of Science PubMed FullText FullText_MUG

 

Co-authors Med Uni Graz

Augustin Christoph

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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.

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