ArticleeLife2021
A deep learning algorithm to translate and classify cardiac electrophysiology.
Article in eLife, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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
23 citing papers in PubMed, 29 citations in OpenAlex.
- A Generative AI Framework to Predict Cardiomyocyte Contraction Function from Single Static Images.bioRxiv : the preprint server for biology · 2026Article
- Machine Learning in Stem Cell Research: From Biological Data to Clinical Translation.Computational and structural biotechnology journal · 2026Review
- Non-Invasive Mapping of Ventricular Action Potential Reconstructed from Contactless Magnetocardiographic Recordings in Intact and Conscious Guinea Pigs.Journal of cardiovascular development and disease · 2025Article
- Non-invasive mapping of ventricular action potential waveforms reconstructed from clinical unshielded magnetocardiography. Potential diagnostic application and current limitations.American heart journal plus : cardiology research and practice · 2025Article
- Computational modelling for improved translation of cardiac inotropic and lusitropic drug effects from rats to humans.Journal of pharmacological and toxicological methods · 2025Article
- An advanced vision of magnetocardiography as an unrivalled method for a more comprehensive non-invasive clinical electrophysiological assessment.American heart journal plus : cardiology research and practice · 2025Article
- A large population of cell-specific action potential models replicating fluorescence recordings of voltage in rabbit ventricular myocytes.Royal Society open science · 2025Article
- The Current State of Realistic Heart Models for Disease Modelling and Cardiotoxicity.International journal of molecular sciences · 2024Review
- Advances in induced pluripotent stem cell-derived cardiac myocytes: technological breakthroughs, key discoveries and new applications.The Journal of physiology · 2024Review
- Detection of biomagnetic signals from induced pluripotent stem cell-derived cardiomyocytes using deep learning with simulation data.Scientific reports · 2024Article
- Toward Digital Twin Technology for Precision Pharmacology.JACC. Clinical electrophysiology · 2024Review
- The use of artificial intelligence in induced pluripotent stem cell-based technology over 10-year period: A systematic scoping review.PloS one · 2024Article
- BeatProfiler: Multimodal In Vitro Analysis of Cardiac Function Enables Machine Learning Classification of Diseases and Drugs.IEEE open journal of engineering in medicine and biology · 2024Article
- A multiscale predictive digital twin for neurocardiac modulation.The Journal of physiology · 2023Article
- Diversity of cells and signals in the cardiovascular system.The Journal of physiology · 2023Article
- Animal Disease Models and Patient-iPS-Cell-Derived In Vitro Disease Models for Cardiovascular Biology-How Close to Disease?Biology · 2023Review
- Emerging methods to model cardiac ion channel and myocyte electrophysiology.Biophysics reviews · 2023Review
- Improving the hERG model fitting using a deep learning-based method.Frontiers in physiology · 2023Article
- Development of automated patch clamp assays to overcome the burden of variants of uncertain significance in inheritable arrhythmia syndromes.Frontiers in physiology · 2023Review
- Biological noise is a key determinant of the reproducibility and adaptability of cardiac pacemaking and EC coupling.The Journal of general physiology · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 4 institutions in 2 countries.
Funding
Abstract
The development of induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) has been a critical in vitro advance in the study of patient-specific physiology, pathophysiology, and pharmacology. We designed a new deep learning multitask network approach intended to address the low throughput, high variability, and immature phenotype of the iPSC-CM platform. The rationale for combining translation and classification tasks is because the most likely application of the deep learning technology we describe here is to translate iPSC-CMs following application of a perturbation. The deep learning network was trained using simulated action potential (AP) data and applied to classify cells into the drug-free and drugged categories and to predict the impact of electrophysiological perturbation across the continuum of aging from the immature iPSC-CMs to the adult ventricular myocytes. The phase of the AP extremely sensitive to perturbation due to a steep rise of the membrane resistance was found to contain the key information required for successful network multitasking. We also demonstrated successful translation of both experimental and simulated iPSC-CM AP data validating our network by prediction of experimental drug-induced effects on adult cardiomyocyte APs by the latter.
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.