ArticleKorean circulation journal2019
Development and Validation of Deep-Learning Algorithm for Electrocardiography-Based Heart Failure Identification.
Article in Korean circulation journal, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 65 papers, 2 of them syntheses that pooled it.
What it found
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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.
AI-ECG Screening for Left Ventricular Systolic Dysfunction: A Prospective, Observational, Multicenter Study
DYNAMIC-AF HF Study: An AI-ECG-Based Approach for Dynamic Assessment of Heart Failure Risk and Myocardial Recovery Following Atrial Fibrillation Ablation
Who cites it
65 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence enabled ECG screening for left ventricular systolic dysfunction: a systematic review.Heart failure reviews · 2023Pooled it
- Electrocardiographic Features of Left Ventricular Diastolic Dysfunction and Heart Failure With Preserved Ejection Fraction: A Systematic Review.Frontiers in cardiovascular medicine · 2021Pooled it
- Application of Artificial Intelligence and Machine Learning in Arrhythmia Detection and Pacemaker Data Analysis.Cardiology research · 2026Review
- Artificial Intelligence-Enabled Electrocardiography Model for Left Ventricular Systolic and Diastolic Dysfunction Predict Incident Atrial Fibrillation in Patients with Sinus Rhythm: A Time-Dependent Analysis.Diagnostics (Basel, Switzerland) · 2026Article
- Review
- Prediction of Left Ventricular Systolic Dysfunction Using an Artificial Intelligence-Based Electrocardiogram Analysis Model in Patients Presenting to the Emergency Department.Diagnostics (Basel, Switzerland) · 2026Article
- Review
- An artificial intelligence model to detect abnormal ejection fraction from non-contrast chest computed tomography: the CT-LVEF study.European heart journal. Digital health · 2026Article
- Multi-Omics and Artificial Intelligence in Cardiovascular Medicine: From Mechanistic Insights to Clinical Translation.Biomedicines · 2026Review
- Machine learning-based stratification of chagas heart failure severity using ECG power spectral biomarkers.Medical & biological engineering & computing · 2026Article
- Heart failure detection in electrocardiograms using artificial intelligence and pragmatic labelling.NPJ digital medicine · 2026Article
- Artificial Intelligence-Driven Electrocardiogram Screening for Asymptomatic Left Ventricular Systolic Dysfunction in the General Population.JACC. Advances · 2026Article
- Development and temporal validation of a two-stage ECG-based machine learning model for LVEF screening: a Middle Eastern cohort study.BMC cardiovascular disorders · 2026Article
- Artificial Intelligence-enhanced Electrocardiography for Heart Failure Screening and Risk Stratification.Current heart failure reports · 2026Review
- Artificial Intelligence-Enabled Electrocardiography in Practice: A State-of-the-Art Review.Korean circulation journal · 2026Review
- Prediction of left ventricular systolic dysfunction in left bundle branch block using a fine-tuned ECG foundation model.Scientific reports · 2026Article
- Artificial Intelligence-Driven Integration of ECG and Molecular Biomarkers in Pulmonary Embolism.International journal of molecular sciences · 2026Review
- Artificial intelligence-enabled electrocardiography from scientific research to clinical application.EMBO molecular medicine · 2026Review
- Estimating LVEF from ECG with GPT-4o Fine-Tuned Vision: A Novel Approach in AI-Driven Cardiac Diagnostics.Journal of medical systems · 2025Article
- A heart failure classification model from radial artery pulse wave using LSTM neural networks.BMC medical informatics and decision making · 2025Article
5 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
objectivesScreening and early diagnosis for heart failure (HF) are critical. However, conventional screening diagnostic methods have limitations, and electrocardiography (ECG)-based HF identification may be helpful. This study aimed to develop and validate a deep-learning algorithm for ECG-based HF identification (DEHF).
methodsThe study involved 2 hospitals and 55,163 ECGs of 22,765 patients who performed echocardiography within 4 weeks were study subjects. ECGs were divided into derivation and validation data. Demographic and ECG features were used as predictive variables. The primary endpoint was detection of HF with reduced ejection fraction (HFrEF; ejection fraction [EF]≤40%), and the secondary endpoint was HF with mid-range to reduced EF (≤50%). We developed the DEHF using derivation data and the algorithm representing the risk of HF between 0 and 1. We confirmed accuracy and compared logistic regression (LR) and random forest (RF) analyses using validation data.
resultsThe area under the receiver operating characteristic curves (AUROCs) of DEHF for identification of HFrEF were 0.843 (95% confidence interval, 0.840-0.845) and 0.889 (0.887-0.891) for internal and external validation, respectively, and these results significantly outperformed those of LR (0.800 [0.797-0.803], 0.847 [0.844-0.850]) and RF (0.807 [0.804-0.810], 0.853 [0.850-0.855]) analyses. The AUROCs of deep learning for identification of the secondary endpoint was 0.821 (0.819-0.823) and 0.850 (0.848-0.852) for internal and external validation, respectively, and these results significantly outperformed those of LR and RF.
conclusionsThe deep-learning algorithm accurately identified HF using ECG features and outperformed other machine-learning methods.
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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.