ArticleStem cell reports2022
Characterizing arrhythmia using machine learning analysis of Ca
Article in Stem cell reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 42 citations in OpenAlex.
- A Deep Learning-Based Scoring Framework for Large-Scale Multi-Donor Cardiotoxicity Screening.bioRxiv : the preprint server for biology · 2026Article
- A simplified co-culture reveals altered cardiotoxic responses to doxorubicin in hPSC-derived cardiomyocytes in the presence of endothelial cells.Stem cell reports · 2026Article
- Interplay between variability in intrinsic cellular properties and heart failure-associated remodeling in a simulated population with human heart failure.Journal of molecular and cellular cardiology · 2026Article
- A Deep-Learning-Based Scoring Framework for Large-Scale Multi-donor Cardiotoxicity Screening.Computational and structural biotechnology journal · 2026Article
- Detection of biomagnetic signals from induced pluripotent stem cell-derived cardiomyocytes using deep learning with simulation data.Scientific reports · 2024Article
- The use of artificial intelligence in induced pluripotent stem cell-based technology over 10-year period: A systematic scoping review.PloS one · 2024Article
- JAK2 as a surface marker for enrichment of human pluripotent stem cells-derived ventricular cardiomyocytes.Stem cell research & therapy · 2023Article
- Evaluating pro-arrhythmogenic effects of the T634S-hERG mutation: insights from a simulation study.Interface focus · 2023Article
- Animal Disease Models and Patient-iPS-Cell-Derived In Vitro Disease Models for Cardiovascular Biology-How Close to Disease?Biology · 2023Review
- Robust generation of human-chambered cardiac organoids from pluripotent stem cells for improved modelling of cardiovascular diseases.Stem cell research & therapy · 2022Article
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate modeling of the heart electrophysiology to predict arrhythmia susceptibility remains a challenge. Current electrophysiological analyses are hypothesis-driven models drawing conclusions from changes in a small subset of electrophysiological parameters because of the difficulty of handling and understanding large datasets. Thus, we develop a framework to train machine learning classifiers to distinguish between healthy and arrhythmic cardiomyocytes using their calcium cycling properties. By training machine learning classifiers on a generated dataset containing a total of 3,003 healthy derived cardiomyocytes and their various arrhythmic states, the multi-class models achieved >90% accuracy in predicting arrhythmia presence and type. We also demonstrate that a binary classifier trained to distinguish cardiotoxic arrhythmia from healthy electrophysiology could determine the key biological changes associated with that specific arrhythmia. Therefore, machine learning algorithms can be used to characterize underlying arrhythmic patterns in samples to improve in vitro preclinical models and complement current in vivo systems.
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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.