Digital twinning of cardiac electrophysiology for congenital heart disease

Journal Article (2024)
Author(s)

Matteo Salvador (Stanford University)

Fanwei Kong (Stanford University)

M. Peirlinck (TU Delft - Medical Instruments & Bio-Inspired Technology)

David W. Parker (Stanford University)

H. Chubb (Stanford University)

A. M. Dubin (Stanford University)

A. L. Marsden (Stanford University)

Research Group
Medical Instruments & Bio-Inspired Technology
DOI related publication
https://doi.org/10.1098/rsif.2023.0729
More Info
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Publication Year
2024
Language
English
Research Group
Medical Instruments & Bio-Inspired Technology
Issue number
215
Volume number
21
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Abstract

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 paediatric 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 using 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 paediatric patients. Identifiability and trustworthiness of calibrated model parameters are assessed by sensitivity analysis and uncertainty quantification.

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