ArticleNature communications2026
Deep learning prediction of left atrial structure and function from 12-lead electrocardiograms.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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.
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Who cites it
3 citing papers in PubMed.
- Deep learning prediction of left atrial structure and function from 12-lead electrocardiograms.Nature communications · 2026Article
- CARDIAC-FM: A Multimodal Foundation Model for Cardiovascular Risk Prediction Using ECG and Cardiac MRI.medRxiv : the preprint server for health sciences · 2026Article
- Renal Function, Atrial Cardiopathy, and Their Joint Association with Mortality in the General Population.Journal of clinical medicine · 2025Article
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15 authors.
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Abstract
Abnormal cardiac atrial structure and function, termed atrial cardiopathy, typically precedes atrial fibrillation and downstream cardiovascular complications, yet detection is limited by the cost and accessibility of high-quality cardiac imaging. Here we show that a deep learning model trained on 12-lead electrocardiograms paired with 21,749 cardiac magnetic resonance scans from the UK Biobank predicts left atrial structure and function. Model-derived measures of atrial cardiopathy are strongly associated with new-onset atrial fibrillation, heart failure, and ischemic stroke after adjustment for clinical risk factors and biomarkers in two external cohorts, with magnitudes comparable to or greater than those for direct imaging measures and clinical risk factors. The risk of cardioembolic stroke, the hallmark complication of atrial fibrillation, increases 66% per standard deviation of left atrial volume. In exploratory analyses, model measures predict cardiac monitor-detected atrial fibrillation more accurately than a clinical risk prediction tool and NT-proBNP levels. This model is an inexpensive, accessible tool that identifies individuals at high-risk for atrial fibrillation and related complications.
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