ArticleFrontiers in cardiovascular medicine2021
EP-PINNs: Cardiac Electrophysiology Characterisation Using Physics-Informed Neural Networks.
Article in Frontiers in cardiovascular medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 52 citations in OpenAlex.
- I-GraphECG: Observability-Based Lead Selection and Interpretable Disease Prediction from the 12-Lead ECG Using a Gray-Box Graph Electrophysiology Surrogate.Sensors (Basel, Switzerland) · 2026Article
- Hyperelastic Cardiovascular NN-FE: A Framework for Integrating Nonlinear Finite Element Solvers with Neural Networks for the Hyperelastic Modeling of Cardiac Valve Tissue.Research square · 2026Article
- A physics-informed eikonal model for simulating arrhythmias in the human heart in real-time.Nature communications · 2026Article
- Artificial Intelligence Enabled Noninvasive Mapping of Cardiac Arrhythmia Origins Using the 12-Lead Electrocardiogram.Journal of cardiovascular electrophysiology · 2026Review
- Artificial Intelligence in Cardiac Electrophysiology: A Clinically Oriented Review with Engineering Primers.Bioengineering (Basel, Switzerland) · 2025Review
- Physics-informed residual learning with spatiotemporal local support for inverse ECG reconstruction.Scientific reports · 2025Article
- Physics-informed neural networks for physiological signal processing and modeling: a narrative review.Physiological measurement · 2025Review
- From PINNs to PIKANs: recent advances in physics-informed machine learning.Machine learning for computational science and engineering · 2025Article
- Computational modelling of biological systems now and then: revisiting tools and visions from the beginning of the century.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Review
- Super-resolution left ventricular flow and pressure mapping by Navier-Stokes-informed neural networks.Computers in biology and medicine · 2025Article
- Intelligent in-cell electrophysiology: Reconstructing intracellular action potentials using a physics-informed deep learning model trained on nanoelectrode array recordings.Nature communications · 2025Article
- Multi-Source Data and Knowledge Fusion via Deep Learning for Dynamical Systems: Applications to Spatiotemporal Cardiac Modeling.IISE transactions on healthcare systems engineering · 2025Article
- PINNing cerebral blood flow: analysis of perfusion MRI in infants using physics-informed neural networks.Frontiers in network physiology · 2025Article
- Reconstruction of excitation waves from mechanical deformation using physics-informed neural networks.Scientific reports · 2024Article
- Super-resolution Left Ventricular Flow and Pressure Mapping by Navier-Stokes-Informed Neural Networks.bioRxiv : the preprint server for biology · 2024Article
- Simultaneous data assimilation and cardiac electrophysiology model correction using differentiable physics and deep learning.Interface focus · 2023Article
- Animal Disease Models and Patient-iPS-Cell-Derived In Vitro Disease Models for Cardiovascular Biology-How Close to Disease?Biology · 2023Review
- Reconstruction of three-dimensional scroll waves in excitable media from two-dimensional observations using deep neural networks.Physical review. E · 2023Article
- Commentary: EP-PINNs: Cardiac electrophysiology characterisation using physics-informed neural networks.Frontiers in cardiovascular medicine · 2022Article
- A Review of Healthy and Fibrotic Myocardium Microstructure Modeling and Corresponding Intracardiac Electrograms.Frontiers in physiology · 2022Review
Corrections and comments
- Commented on by
Authors and funding
7 authors at 2 institutions in 3 countries.
Funding
Abstract
Accurately inferring underlying electrophysiological (EP) tissue properties from action potential recordings is expected to be clinically useful in the diagnosis and treatment of arrhythmias such as atrial fibrillation. It is, however, notoriously difficult to perform. We present EP-PINNs (Physics Informed Neural Networks), a novel tool for accurate action potential simulation and EP parameter estimation from sparse amounts of EP data. We demonstrate, using 1D and 2D
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.