ArticleESC heart failure2025
Electrocardiograph analysis for risk assessment of heart failure with preserved ejection fraction: A deep learning model.
Article in ESC heart failure, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence Applied to Electrocardiogram-Based Cardiovascular Risk Assessment: A Systematic Review.Arquivos brasileiros de cardiologia · 2026Pooled it
- Explainable Lightweight AI for the Identification of Right-Sided Cardiac Dysfunction in a Saudi Arabian Diabetic Cohort.Journal of clinical medicine · 2026Article
- Implementation of Machine Learning in Heart Failure Trials.Current heart failure reports · 2026Review
- Artificial intelligence in heart failure.The Egyptian heart journal : (EHJ) : official bulletin of the Egyptian Society of Cardiology · 2026Review
- Artificial Intelligence-Enhanced Electrocardiography for the Diagnosis of Heart Failure With Preserved Ejection Fraction: A Systematic Review and Meta-Analysis.Cardiology research and practice · 2026Article
- Heart Failure in the Modern Era: A Narrative Overview of Recent Research from 2022-2025.Journal of cardiovascular development and disease · 2025Review
- PSO-BiLSTM-Attention: An Interpretable Deep Learning Model Optimized by Particle Swarm Optimization for Accurate Ischemic Heart Disease Incidence Forecasting.Bioengineering (Basel, Switzerland) · 2025Article
- Use of artificial intelligence for detecting left ventricular dysfunction and predicting incident heart failure risk.ESC heart failure · 2025Review
- Electrocardiograph analysis for risk assessment of heart failure with preserved ejection fraction: A deep learning model.ESC heart failure · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
aimsHeart failure with preserved ejection fraction (HFpEF) requires an efficient screening method. We developed a deep learning model (DLM) to screen HFpEF risk using electrocardiograms (ECGs). METHODS AND
resultsA cohort study was conducted utilising data from Cohorts A and B. A convolutional neural network-long short-term memory (CNN-LSTM) DLM was employed. HFpEF risk was determined by left ventricular end-diastolic pressure (LVEDP) and clinical symptoms. The DLM was trained by ECGs. LVEDP for each patient was collected through invasive left ventricular catheterisation. Cohort A and B comprised data from individuals at high risk for HFpEF (LVEDP > 12 mmHg) and low risk for HFpEF (LVEDP ≤ 12 mmHg). The model was trained on Cohort A and prospectively validated on Cohort B.
resultsA total of 238 patients underwent ECG and left ventricular catheterisation for model training in Cohort A, and 117 patients for validation in Cohort B. The DLM achieved 78% accuracy in assessing HFpEF risk in Cohort A, while in Cohort B, it demonstrated 78% accuracy, 71.9% specificity, and 71.7% sensitivity. In the validation Cohort B, the DLM-identified high-risk HFpEF group exhibited significantly higher prevalence of diabetes (22.03%-11.86%, P < 0.01), higher BMI indices (25.92-24.22 kg/cm
conclusionsThe DLM offers an accurate, cost-effective tool for HFpEF risk assessment, potentially facilitating early detection and improved clinical management.
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