RT Journal Article SR Electronic T1 Digital twinning of cardiac electrophysiology for congenital heart disease JF bioRxiv FD Cold Spring Harbor Laboratory SP 2023.11.27.568942 DO 10.1101/2023.11.27.568942 A1 Salvador, Matteo A1 Kong, Fanwei A1 Peirlinck, Mathias A1 Parker, David W. A1 Chubb, Henry A1 Dubin, Anne M. A1 Marsden, Alison Lesley YR 2023 UL http://biorxiv.org/content/early/2023/11/28/2023.11.27.568942.abstract AB 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.Competing Interest StatementThe authors have declared no competing interest.