ArticleFrontiers in cardiovascular medicine2022
Electrocardiogram-Based Heart Age Estimation by a Deep Learning Model Provides More Information on the Incidence of Cardiovascular Disorders.
Article in Frontiers in cardiovascular medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 2 of them syntheses that pooled it.
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
46 citing papers in PubMed, 2 syntheses or guidelines pooled it, 81 citations in OpenAlex.
- Artificial Intelligence-Derived ECG-Age as a Predictor of Mortality and Cardiovascular Events: A Systematic Review and Meta-Analysis.Arquivos brasileiros de cardiologia · 2026Pooled it
- Deep learning and electrocardiography: systematic review of current techniques in cardiovascular disease diagnosis and management.Biomedical engineering online · 2025Pooled it
- Explainable age prediction using a transformer trained on diverse global electrocardiograms.European heart journal. Digital health · 2026Article
- Deep learning analysis of 12-lead electrocardiograms for bloodstream infection prediction: a multi-center validation study.BMC medical informatics and decision making · 2026Article
- Artificial intelligence-derived electrocardiographic age gap as a predictor of mortality after coronary revascularization: prognostic value and short-term intra-patient variability.European heart journal. Digital health · 2026Article
- Electrocardiogram derived heart age models agreement, accuracy and predictive ability in the Tromsø study.npj aging · 2026Article
- Non-invasive hemoglobin estimation with outcome prediction via deep learning analysis of ECG-derived cardiac micro-dynamics.Internal and emergency medicine · 2026Article
- Artificial intelligence-predicted ECG age gap as a biomarker: bias-adjusted correlation with mortality and cardiovascular risk factors.European heart journal. Digital health · 2026Article
- CardioMetAge estimates cardiometabolic aging and predicts disease outcomes.BMC medicine · 2026Article
- Factors associated with physician modifications to automated ECG interpretations.European heart journal. Digital health · 2026Article
- Artificial intelligence-enabled electrocardiography from scientific research to clinical application.EMBO molecular medicine · 2026Review
- Artificial Intelligence-Enabled Electrocardiography Identifies Osteoporosis and has Prognostic Value.Journal of medical systems · 2025Article
- AI-ECG-derived biological age as a predictor of mortality in cardiovascular and acute care patients.European heart journal. Digital health · 2025Article
- Comparison of Artificial Intelligence-Derived Heart Age with Chronological Age Using Normal Sinus Electrocardiograms in Patients with No Evidence of Cardiac Disease.Journal of clinical medicine · 2025Article
- Article
- Electrocardiogram abnormalities and CVD mortality: prospective cohort study of Tehran lipid and glucose study.BMC public health · 2025Article
- Determinants of artificial intelligence electrocardiogram-derived age and its association with cardiovascular events and mortality: a systematic review and meta-analysis.NPJ digital medicine · 2025Article
- Cardiovascular magnetic resonance imaging markers of ageing: a multi-centre, cross-sectional cohort study.European heart journal open · 2025Article
- Deep learning for electrocardiogram interpretation: Bench to bedside.European journal of clinical investigation · 2025Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe biological age progression of the heart varies from person to person. We developed a deep learning model (DLM) to predict the biological age
methodsThere were 71,741 cases ranging from 20 to 80 years old recruited from the health examination center. The development set used 32,707 cases to train the DLM for estimating the ECG-age, and 8,295 cases were used as the tuning set. The validation set included 30,469 ECGs to follow the outcomes, including all-cause mortality, cardiovascular-cause mortality, heart failure (HF), diabetes mellitus (DM), chronic kidney disease (CKD), acute myocardial infarction (AMI), stroke (STK), coronary artery disease (CAD), atrial fibrillation (AF), and hypertension (HTN). Two independent external validation sets (SaMi-Trop and CODE15) were also used to validate our DLM.
resultsThe mean absolute errors of chronologic age and ECG-age was 6.899 years (
conclusionsThe ECG-age estimated
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.