ArticleInternational journal of cardiology2021
External validation of a deep learning electrocardiogram algorithm to detect ventricular dysfunction.
Article in International journal of cardiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 3 of them syntheses that pooled it.
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Who cites it
52 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Deep learning and electrocardiography: systematic review of current techniques in cardiovascular disease diagnosis and management.Biomedical engineering online · 2025Pooled it
- Machine learning based insights into cardiomyopathy and heart failure research: a bibliometric analysis from 2005 to 2024.Frontiers in medicine · 2025Pooled it
- Artificial intelligence enabled ECG screening for left ventricular systolic dysfunction: a systematic review.Heart failure reviews · 2023Pooled it
- External validation of artificial intelligence-guided electrocardiogram (AI-ECG) in detecting asymptomatic left ventricular dysfunction: A systematic review and meta-analysis.American journal of preventive cardiology · 2027Article
- Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial intelligence-enabled electrocardiography to triage echocardiography for structural heart disease diagnosis in a low-resource setting.American journal of preventive cardiology · 2026Article
- Artificial Intelligence-Enhanced Electrocardiography for Predicting Paroxysmal Atrial Fibrillation From Sinus Rhythm: Impact of Data Integration Across Institutions and Devices.Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc · 2026Article
- Detection and prognostic stratification of left ventricular systolic dysfunction in left bundle branch block using an artificial intelligence-enabled electrocardiography.Journal of cardiovascular imaging · 2026Article
- Artificial Intelligence and the Expanding Universe of Cardio-Oncology: Beyond Detection Toward Prediction and Prevention of Therapy-Related Cardiotoxicity-A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2026Review
- Multisite, External Validation of an AI-Enabled ECG Algorithm for Detection of Low Ejection Fraction.JACC. Advances · 2026Article
- Prediction of left ventricular systolic dysfunction in left bundle branch block using a fine-tuned ECG foundation model.Scientific reports · 2026Article
- A Screening Method for Determining Left Ventricular Systolic Function Based on Spectral Analysis of a Single-Channel Electrocardiogram Using Machine Learning Algorithms.Diagnostics (Basel, Switzerland) · 2026Article
- AI Applications in Electrocardiography for Ischemic and Structural Heart Disease: A Review of the Current State.Journal of clinical medicine · 2026Review
- Artificial Intelligence-Enabled Electrocardiography Identifies Osteoporosis and has Prognostic Value.Journal of medical systems · 2025Article
- PrevCardioOncAI: Machine Learning Algorithms for Predicting Cardiovascular Disease in Cancer Survivors.Journal of the American Heart Association · 2025Observational
- Review
- Artificial Intelligence for Cardiovascular Care in Action: From Learning to Implementation in Health Systems.JACC. Advances · 2025Review
- An Artificial Intelligence-Enabled Electrocardiogram to Evaluate Patients With Dyspnea in the Emergency Department.Mayo Clinic proceedings. Innovations, quality & outcomes · 2025Article
- Utility of convolutional neural network-enhanced electrocardiogram to diagnose and predict mitral regurgitation in patients with chronic atrial fibrillation.Heart and vessels · 2025Article
- Classification of mitral regurgitation in echocardiography based on deep learning methods.Quantitative imaging in medicine and surgery · 2025Article
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16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo validate a novel artificial-intelligence electrocardiogram algorithm (AI-ECG) to detect left ventricular systolic dysfunction (LVSD) in an external population.
backgroundLVSD, even when asymptomatic, confers increased morbidity and mortality. We recently derived AI-ECG to detect LVSD using ECGs based on a large sample of patients treated at the Mayo Clinic.
methodsWe performed an external validation study with subjects from the Know Your Heart Study, a cross-sectional study of adults aged 35-69 years residing in two cities in Russia, who had undergone both ECG and transthoracic echocardiography. LVSD was defined as left ventricular ejection fraction ≤ 35%. We assessed the performance of the AI-ECG to identify LVSD in this distinct patient population.
resultsAmong 4277 subjects in this external population-based validation study, 0.6% had LVSD (compared to 7.8% of the original clinical derivation study). The overall performance of the AI-ECG to detect LVSD was robust with an area under the receiver operating curve of 0.82. When using the LVSD probability cut-off of 0.256 from the original derivation study, the sensitivity, specificity, and accuracy in this population were 26.9%, 97.4%, 97.0%, respectively. Other probability cut-offs were analysed for different sensitivity values.
conclusionsThe AI-ECG detected LVSD with robust test performance in a population that was very different from that used to develop the algorithm. Population-specific cut-offs may be necessary for clinical implementation. Differences in population characteristics, ECG and echocardiographic data quality may affect test performance.
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