Branched Latent Neural Maps (BLNMs) provided a geometry-specific surrogate model of cardiac function for fast and robust parameter estimation to match clinical ECGs in pediatric patients.
A novel computational pipeline using machine learning can create patient-specific digital twins of cardiac electrophysiology for pediatric congenital heart disease.
In recent years, blending mechanistic knowledge with machine learning has had a major impact in digital healthcare. In this work, we introduce a computational pipeline to build certified digital replicas of cardiac electrophysiology in pediatric patients with congenital heart disease. We construct the patient-specific geometry by means of semi-automatic segmentation and meshing tools. We generate a dataset of electrophysiology simulations covering cell-to-organ level model parameters and utilizing rigorous mathematical models based on differential equations. We previously proposed Branched Latent Neural Maps (BLNMs) as an accurate and efficient means to recapitulate complex physical processes in a neural network. Here, we employ BLNMs to encode the parametrized temporal dynamics of in silico 12-lead electrocardiograms (ECGs). BLNMs act as a geometry-specific surrogate model of cardiac function for fast and robust parameter estimation to match clinical ECGs in pediatric patients. Identifiability and trustworthiness of calibrated model parameters are assessed by sensitivity analysis and uncertainty quantification.
Salvador et al. (Tue,) conducted a other in Congenital heart disease. Branched Latent Neural Maps (BLNMs) for digital twinning was evaluated on Parameter estimation to match clinical ECGs. Branched Latent Neural Maps (BLNMs) provided a geometry-specific surrogate model of cardiac function for fast and robust parameter estimation to match clinical ECGs in pediatric patients.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: