ArticleNature communications2022
Automated multilabel diagnosis on electrocardiographic images and signals.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
65 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial Intelligence in the Heart of Medicine: A Systematic Approach to Transforming Arrhythmia Care with Intelligent Systems.Current cardiology reviews · 2025Pooled it
- Diagnostic Accuracy of AI Algorithms in Aortic Stenosis Screening: A Systematic Review and Meta-Analysis.Clinical medicine & research · 2024Pooled it
- Multimodal artificial intelligence to predict cardiomyopathy development in hereditary transthyretin amyloidosis.Open heart · 2026Article
- Artificial intelligence and the evolution of the electrocardiogram: from cardiovascular diagnostic tool to digital biomarker.European heart journal. Digital health · 2026Review
- Disease-focused evaluation of AI-ECG tools: clarifying study design, findings, and the principles of open science.European heart journal. Digital health · 2026Article
- Article
- Source-Only Transportability of Engineered ECG Features for Healthy-Versus-Myocardial Infarction Classification.Diagnostics (Basel, Switzerland) · 2026Article
- pedQTNet: A Deep Learning Approach to Estimate Corrected QT Intervals from Multi-Lead Conventional ECG Waveforms in Pediatric Patients.Journal of medical systems · 2026Article
- Artificial intelligence-enabled electrocardiography to triage echocardiography for structural heart disease diagnosis in a low-resource setting.American journal of preventive cardiology · 2026Article
- Wearable-Echo-FM: an ECG echo foundation model for 1-lead electrocardiography.European heart journal. Digital health · 2026Article
- Open science requires trust and rigour: a framework for responsible evaluation of shared AI-ECG tools.European heart journal. Digital health · 2026Article
- Using Artificial Intelligence to Adjudicate Major Adverse Cardiovascular Events in Clinical Trials.Journal of the American College of Cardiology · 2026Article
- Artificial intelligence-based automated interpretation of images of electrocardiograms: development and multinational validation of ECG-GPT.European heart journal. Digital health · 2026Article
- Teaching multimodal LLMs to comprehend 12-lead electrocardiographic images.NPJ digital medicine · 2026Article
- Artificial Intelligence-Enabled ECG to Detect Congenitally Corrected Transposition of the Great Arteries.Pediatric cardiology · 2026Article
- Evaluation of artificial intelligence-based electrocardiogram analysis tools in patients with hypertrophic cardiomyopathy.European heart journal. Digital health · 2026Article
- MM-GradCAM: an improved multimodal GradCAM method with 1D and 2D ECG data for detection of cardiac arrhythmia.Scientific reports · 2026Article
- Tele-Electrocardiography and Mortality: Clinical Outcomes in Digital Electrocardiography Cohort-Data from Belo Horizonte, Brazil (CODE-BH).Global heart · 2026Article
- Artificial Intelligence-Enabled 8-Channel ECG Diagnosing of Abnormalities with Wide QRS Complexes.Health data science · 2026Article
- Cardiac health assessment across scenarios and devices using a multimodal foundation model pretrained on data from 1.7 million individuals.Nature machine intelligence · 2026Article
5 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
The application of artificial intelligence (AI) for automated diagnosis of electrocardiograms (ECGs) can improve care in remote settings but is limited by the reliance on infrequently available signal-based data. We report the development of a multilabel automated diagnosis model for electrocardiographic images, more suitable for broader use. A total of 2,228,236 12-lead ECGs signals from 811 municipalities in Brazil are transformed to ECG images in varying lead conformations to train a convolutional neural network (CNN) identifying 6 physician-defined clinical labels spanning rhythm and conduction disorders, and a hidden label for gender. The image-based model performs well on a distinct test set validated by at least two cardiologists (average AUROC 0.99, AUPRC 0.86), an external validation set of 21,785 ECGs from Germany (average AUROC 0.97, AUPRC 0.73), and printed ECGs, with performance superior to signal-based models, and learning clinically relevant cues based on Grad-CAM. The model allows the application of AI to ECGs across broad settings.
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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.