ArticleFunctional imaging and modeling of the heart : ... International Workshop, FIMH ..., proceedings. FIMH (Conference)2021
Learning atrial fiber orientations and conductivity tensors from intracardiac maps using physics-informed neural networks.
Article in Functional imaging and modeling of the heart : ... International Workshop, FIMH ..., proceedings. FIMH (Conference), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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The trial behind it
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Who cites it
11 citing papers in PubMed.
- Artificial Intelligence in Cardiac Electrophysiology: A Clinically Oriented Review with Engineering Primers.Bioengineering (Basel, Switzerland) · 2025Review
- Physics-informed neural networks for physiological signal processing and modeling: a narrative review.Physiological measurement · 2025Review
- Super-resolution left ventricular flow and pressure mapping by Navier-Stokes-informed neural networks.Computers in biology and medicine · 2025Article
- Super-resolution Left Ventricular Flow and Pressure Mapping by Navier-Stokes-Informed Neural Networks.bioRxiv : the preprint server for biology · 2024Article
- A Review of Personalised Cardiac Computational Modelling Using Electroanatomical Mapping Data.Arrhythmia & electrophysiology review · 2024Review
- Primer on Machine Learning in Electrophysiology.Arrhythmia & electrophysiology review · 2023Review
- Manifold Approximating Graph Interpolation of Cardiac Local Activation Time.IEEE transactions on bio-medical engineering · 2022Article
- Atrial conduction velocity mapping: clinical tools, algorithms and approaches for understanding the arrhythmogenic substrate.Medical & biological engineering & computing · 2022Review
- Commentary: EP-PINNs: Cardiac electrophysiology characterisation using physics-informed neural networks.Frontiers in cardiovascular medicine · 2022Article
- EP-PINNs: Cardiac Electrophysiology Characterisation Using Physics-Informed Neural Networks.Frontiers in cardiovascular medicine · 2021Article
- Extrapolation of Ventricular Activation Times From Sparse Electroanatomical Data Using Graph Convolutional Neural Networks.Frontiers in physiology · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Electroanatomical maps are a key tool in the diagnosis and treatment of atrial fibrillation. Current approaches focus on the activation times recorded. However, more information can be extracted from the available data. The fibers in cardiac tissue conduct the electrical wave faster, and their direction could be inferred from activation times. In this work, we employ a recently developed approach, called physics informed neural networks, to learn the fiber orientations from electroanatomical maps, taking into account the physics of the electrical wave propagation. In particular, we train the neural network to weakly satisfy the anisotropic eikonal equation and to predict the measured activation times. We use a local basis for the anisotropic conductivity tensor, which encodes the fiber orientation. The methodology is tested both in a synthetic example and for patient data. Our approach shows good agreement in both cases and it outperforms a state of the art method in the patient data. The results show a first step towards learning the fiber orientations from electroanatomical maps with physics-informed neural networks.
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Registered trials
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