An efficient end-to-end computational framework for the generation of ECG calibrated volumetric models of human atrial electrophysiology
🤖 AI Summary
Current human atrial electrophysiological (EP) computational models face three major bottlenecks for regulatory-grade digital twin development and virtual cohort construction: anatomical modeling inaccuracy, difficulty calibrating sparse clinical data, and low simulation efficiency. To address these, we propose an end-to-end computational framework featuring: (i) automated multi-scale bi-atrial volumetric reconstruction; (ii) spatially varying EP parameter field definition; (iii) parametric modeling of inter-atrial conduction pathways; and (iv) efficient EP–ECG coupled simulation. Integrating image segmentation, fiber orientation modeling, parametric inverse inference, reaction–eikonal/reaction–diffusion forward modeling, and high-fidelity ECG computation, the framework generates high-quality personalized meshes from 50 atrial fibrillation patient datasets, enabling multi-model co-simulation. The resulting digital twin supports ECG-driven, high-accuracy, parameter-controllable atrial activation simulations—substantially enhancing clinical interpretability and regulatory compliance.
Application Category
📝 Abstract
Computational models of atrial electrophysiology (EP) are increasingly utilized for applications such as the development of advanced mapping systems, personalized clinical therapy planning, and the generation of virtual cohorts and digital twins. These models have the potential to establish robust causal links between simulated in silico behaviors and observed human atrial EP, enabling safer, cost-effective, and comprehensive exploration of atrial dynamics. However, current state-of-the-art approaches lack the fidelity and scalability required for regulatory-grade applications, particularly in creating high-quality virtual cohorts or patient-specific digital twins. Challenges include anatomically accurate model generation, calibration to sparse and uncertain clinical data, and computational efficiency within a streamlined workflow. This study addresses these limitations by introducing novel methodologies integrated into an automated end-to-end workflow for generating high-fidelity digital twin snapshots and virtual cohorts of atrial EP. These innovations include: (i) automated multi-scale generation of volumetric biatrial models with detailed anatomical structures and fiber architecture; (ii) a robust method for defining space-varying atrial parameter fields; (iii) a parametric approach for modeling inter-atrial conduction pathways; and (iv) an efficient forward EP model for high-fidelity electrocardiogram computation. We evaluated this workflow on a cohort of 50 atrial fibrillation patients, producing high-quality meshes suitable for reaction-eikonal and reaction-diffusion models and demonstrating the ability to simulate atrial ECGs under parametrically controlled conditions. These advancements represent a critical step toward scalable, precise, and clinically applicable digital twin models and virtual cohorts, enabling enhanced patient-specific predictions and therapeutic planning.
Problem
Research questions and friction points this paper is trying to address.
Develop ECG-calibrated atrial electrophysiology models
Improve fidelity and scalability for clinical applications
Automate high-fidelity digital twin and cohort generation
Innovation
Methods, ideas, or system contributions that make the work stand out.
Automated multi-scale volumetric biatrial models
Robust space-varying atrial parameter fields
Efficient forward EP model for ECG computation
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