ArticleInternational journal of cardiology. Heart & vasculature2023
Cardiovascular events and artificial intelligence-predicted age using 12-lead electrocardiograms.
Article in International journal of cardiology. Heart & vasculature, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in cardiovascular diagnostics: a systematic review and descriptive analysis of clinical applications and diagnostic performance.BMC cardiovascular disorders · 2025Pooled it
- Artificial intelligence-predicted ECG age gap as a biomarker: bias-adjusted correlation with mortality and cardiovascular risk factors.European heart journal. Digital health · 2026Article
- AI-ECG-derived biological age as a predictor of mortality in cardiovascular and acute care patients.European heart journal. Digital health · 2025Article
- Utility of convolutional neural network-enhanced electrocardiogram to diagnose and predict mitral regurgitation in patients with chronic atrial fibrillation.Heart and vessels · 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
- 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
- Evaluating convolutional neural network-enhanced electrocardiography for hypertrophic cardiomyopathy detection in a specialized cardiovascular setting.Heart and vessels · 2024Article
- Revolutionizing Cardiology through Artificial Intelligence-Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment-A Comprehensive Review of the Past 5 Years.Diagnostics (Basel, Switzerland) · 2024Review
- Unlocking the potential of artificial intelligence in electrocardiogram biometrics: age-related changes, anomaly detection, and data authenticity in mobile health platforms.European heart journal. Digital health · 2024Article
- Identifying patients with acute aortic dissection using an electrocardiogram with convolutional neural network.International journal of cardiology. Heart & vasculature · 2024Article
- What Else Can AI See in a Digital ECG?Journal of personalized medicine · 2023Review
- Identification of patients with dilated phase of hypertrophic cardiomyopathy using a convolutional neural network applied to multiple, dual, and single lead electrocardiograms.International journal of cardiology. Heart & vasculature · 2023Article
- DNN-derived electrocardiographic age is associated with the atrial fibrillation risk in a Chinese population.Digital healthArticle
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: There is increasing evidence that 12-lead electrocardiograms (ECG) can be used to predict biological age, which is associated with cardiovascular events. However, the utility of artificial intelligence (AI)-predicted age using ECGs remains unclear. Methods: Using a single-center database, we developed an AI-enabled ECG using 17 042 sinus rhythm ECGs (SR-ECG) to predict chronological age (CA) with a convolutional neural network that yields AI-predicted age. Using the 5-fold cross validation method, AI-predicted age deriving from the test dataset was yielded for all ECGs. The incidence by AgeDiff and the areas under the curve by receiver operating characteristic curve with AI-predicted age for cardiovascular events were analyzed. Results: During the mean follow-up period of 460.1 days, there were 543 cardiovascular events. The annualized incidence of cardiovascular events was 2.24 %, 2.44 %, and 3.01 %/year for patients with AgeDiff < -6, -6 to ≤6, and >6 years, respectively. The areas under the curve for cardiovascular events with CA and AI-predicted age, respectively, were 0.673 and 0.679 (Delong's test, P = 0.388) for all patients; 0.642 and 0.700 (P = 0.003) for younger patients (CA < 60 years); and 0.584 and 0.570 (P = 0.268) for older patients (CA ≥ 60 years). Conclusions: AI-predicted age using 12-lead ECGs showed superiority in predicting cardiovascular events compared with CA in younger patients, but not in older patients.
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