A unified framework for geometry-independent operator learning in cardiac electrophysiology simulations

• URL: http://arxiv.org/abs/2512.01702v1
• Date: Mon, 01 Dec 2025 14:07:39 GMT
• Title: A unified framework for geometry-independent operator learning in cardiac electrophysiology simulations
• Authors: Bei Zhou, Cesare Corrado, Shuang Qian, Maximilian Balmus, Angela W. C. Lee, Cristobal Rodero, Marco J. W. Gotte, Luuk H. G. A. Hopman, Mengyun Qiao, Steven Niederer,
• Abstract summary: We introduce a geometry-independent operator-learning framework that predicts local activation time fields.<n>We generated a dataset of 308,700 simulations using a GPU-accelerated electrophysiology solver.<n>Our framework establishes a general strategy for learning domain-invariant biophysical mappings across variable anatomical domains.
• Score: 1.9369390063413154
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate maps of atrial electrical activation are essential for personalised treatment of arrhythmias, yet biophysically detailed simulations remain computationally intensive for real-time clinical use or population-scale analyses. Here we introduce a geometry-independent operator-learning framework that predicts local activation time (LAT) fields across diverse left atrial anatomies with near-instantaneous inference. We generated a dataset of 308,700 simulations using a GPU-accelerated electrophysiology solver, systematically varying multiple pacing sites and physiologically varied conduction properties across 147 patient-specific geometries derived from two independent clinical cohorts. All anatomical and functional data are expressed in a Universal Atrium Coordinate system, providing a consistent representation that decouples electrophysiological patterns from mesh topology. Within this coordinate space, we designed a neural operator with a vision-transformer backbone to learn the mapping from structural and electrophysiological inputs to LAT fields. With a mean prediction error of 5.1 ms over a 455 ms maximum simulation time, the model outperforms established operator-learning approaches and performs inference in 0.12 ms per sample. Our framework establishes a general strategy for learning domain-invariant biophysical mappings across variable anatomical domains and enables integration of computational electrophysiology into real-time and large-scale clinical workflows.

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