ArticleScientific reports2022
Deep learning of ECG waveforms for diagnosis of heart failure with a reduced left ventricular ejection fraction.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Within a Heartbeat: A Machine Learning Approach Using VCG for Heart Failure Assessment in Sinus Rhythm.Journal of personalized medicine · 2026Article
- Automated HFrEF Diagnosis Using an Optimized TimeSformer Model in Echocardiography.Journal of imaging informatics in medicine · 2026Article
- Differentiating HFmr/rEF from HFpEF using standard 12-lead ECG measurements: an interpretable machine learning study.Open heart · 2026Article
- Machine learning-based stratification of chagas heart failure severity using ECG power spectral biomarkers.Medical & biological engineering & computing · 2026Article
- The Evolving Utility of Artificial Intelligence-Based Tools for the Detection of Heart Failure and Cardiomyopathies: From Potential to Implementation.Current heart failure reports · 2026Review
- Deep Learning Model Using Transfer Learning for Detecting Left Ventricular Systolic Dysfunction: Retrospective Algorithm Development and Validation Study.JMIR medical informatics · 2026Article
- Implementation of Machine Learning in Heart Failure Trials.Current heart failure reports · 2026Review
- 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
- Long-term, ambulatory 12-lead ECG from a single non-standard lead using perceptual reconstruction.medRxiv : the preprint server for health sciences · 2025Article
- A heart failure classification model from radial artery pulse wave using LSTM neural networks.BMC medical informatics and decision making · 2025Article
- Novel artificial intelligence model using electrocardiogram for detecting acute myocardial infarction needing revascularization.European heart journal. Digital health · 2025Article
- Artificial intelligence in ECG-based diagnosis of low left ventricular ejection fraction: a systematic review and meta-analysis.Biomedical engineering letters · 2025Review
- Artificial Intelligence Tools for Preconception Cardiomyopathy Screening Among Women of Reproductive Age.Annals of family medicine · 2025Article
- Advances in Electrocardiogram-Based Artificial Intelligence Reveal Multisystem Biomarkers.Journal of clinical & experimental cardiology · 2025Article
- Diagnosis and Treatment of Acute Heart Failure: A Retrospective Observational Study and Medical Audit.Journal of clinical medicine · 2024Article
- Revolutionising Acute Cardiac Care With Artificial Intelligence: Opportunities and Challenges.The Canadian journal of cardiology · 2024Review
- Artificial Intelligence-Based Electrocardiographic Biomarker for Outcome Prediction in Patients With Acute Heart Failure: Prospective Cohort Study.Journal of medical Internet research · 2024Observational
- Development and validation of machine learning algorithms based on electrocardiograms for cardiovascular diagnoses at the population level.NPJ digital medicine · 2024Article
- Estimating Ejection Fraction from the 12 Lead ECG among Patients with Acute Heart Failure.medRxiv : the preprint server for health sciences · 2024Article
- Application and Potential of Artificial Intelligence in Heart Failure: Past, Present, and Future.International journal of heart failure · 2024Review
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The performance and clinical implications of the deep learning aided algorithm using electrocardiogram of heart failure (HF) with reduced ejection fraction (DeepECG-HFrEF) were evaluated in patients with acute HF. The DeepECG-HFrEF algorithm was trained to identify left ventricular systolic dysfunction (LVSD), defined by an ejection fraction (EF) < 40%. Symptomatic HF patients admitted at Seoul National University Hospital between 2011 and 2014 were included. The performance of DeepECG-HFrEF was determined using the area under the receiver operating characteristic curve (AUC) values. The 5-year mortality according to DeepECG-HFrEF results was analyzed using the Kaplan-Meier method. A total of 690 patients contributing 18,449 ECGs were included with final 1291 ECGs eligible for the study (mean age 67.8 ± 14.4 years; men, 56%). HFrEF (+) identified an EF < 40% and HFrEF (-) identified EF ≥ 40%. The AUC value was 0.844 for identifying HFrEF among patients with acute symptomatic HF. Those classified as HFrEF (+) showed lower survival rates than HFrEF (-) (log-rank p < 0.001). The DeepECG-HFrEF algorithm can discriminate HFrEF in a real-world HF cohort with acceptable performance. HFrEF (+) was associated with higher mortality rates. The DeepECG-HFrEF algorithm may help in identification of LVSD and of patients at risk of worse survival in resource-limited settings.
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