ArticleCirculation. Genomic and precision medicine2023
Genetic Susceptibility to Atrial Fibrillation Identified via Deep Learning of 12-Lead Electrocardiograms.
Article in Circulation. Genomic and precision medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07163767 (Acute Myocardial Infarction Prediction Using Artificial Intelligence Applied to Electrocardiogram Images), which is not on this map. Cited by 9 papers.
What it found
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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.
Acute Myocardial Infarction Prediction Using Artificial Intelligence Applied to Electrocardiogram Images
Who cites it
9 citing papers in PubMed, 11 citations in OpenAlex.
- Association between polygenic risk scores and cardiovascular events in prostate cancer patients receiving androgen deprivation therapy in Han Chinese.Cardio-oncology (London, England) · 2026Article
- Causal Effects of Atrial Fibrillation and Warfarin Use on Osteoporosis Risk: A Two-Sample Mendelian Randomization Analysis.International journal of genomics · 2026Article
- Hierarchical Molecular Language Models (HMLMs).ArXiv · 2025Article
- Cardiac arrhythmia in individuals with steroid sulfatase deficiency (X-linked ichthyosis): candidate anatomical and biochemical pathways.Essays in biochemistry · 2024Review
- Integrating Clinical, Genetic, and Electrocardiogram-Based Artificial Intelligence to Estimate Risk of Incident Atrial Fibrillation.medRxiv : the preprint server for health sciences · 2024Article
- Innovative approaches to atrial fibrillation prediction: should polygenic scores and machine learning be implemented in clinical practice?Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2024Review
- Artificial Intelligence and Heart-Brain Connections: A Narrative Review on Algorithms Utilization in Clinical Practice.Healthcare (Basel, Switzerland) · 2024Review
- Consider this a WARNing.Patterns (New York, N.Y.) · 2024Article
- Genetic Architectures of Medical Images Revealed by Registration of Multiple Modalities.Bioinformatics and biology insights · 2024Article
Corrections and comments
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Authors and funding
18 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundArtificial intelligence (AI) models applied to 12-lead ECG waveforms can predict atrial fibrillation (AF), a heritable and morbid arrhythmia. However, the factors forming the basis of risk predictions from AI models are usually not well understood. We hypothesized that there might be a genetic basis for an AI algorithm for predicting the 5-year risk of new-onset AF using 12-lead ECGs (ECG-AI)-based risk estimates.
methodsWe applied a validated ECG-AI model for predicting incident AF to ECGs from 39 986 UK Biobank participants without AF. We then performed a genome-wide association study (GWAS) of the predicted AF risk and compared it with an AF GWAS and a GWAS of risk estimates from a clinical variable model.
resultsIn the ECG-AI GWAS, we identified 3 signals (
conclusionsPredicted AF risk from an ECG-AI model is influenced by genetic variation implicating sarcomeric, ion channel and body height pathways. ECG-AI models may identify individuals at risk for disease via specific biological pathways.
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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.