ArticleEuropean heart journal. Digital health2021
Artificial intelligence assessment for early detection of heart failure with preserved ejection fraction based on electrocardiographic features.
Article in European heart journal. Digital health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. It is linked to trial NCT07708818 (DYNAMIC-AF HF Study), which is not on this map. Cited by 43 papers.
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.
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
43 citing papers in PubMed, 64 citations in OpenAlex.
- Artificial intelligence-enabled electrocardiography for detection of left ventricular diastolic dysfunction: a systematic review and meta-analysis.European heart journal. Digital health · 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
- Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease.Diagnostics (Basel, Switzerland) · 2026Review
- Automated HFrEF Diagnosis Using an Optimized TimeSformer Model in Echocardiography.Journal of imaging informatics in medicine · 2026Article
- An Artificial Intelligence Model for ECG-Based Prediction of Heart Failure with Preserved Ejection Fraction Diagnosis.Journal of cardiovascular development and disease · 2026Article
- Review
- LightGBM-Based Classification of Heart Failure Phenotypes Using Morpho-Energy Features from High-Resolution ECG.Sensors (Basel, Switzerland) · 2026Article
- Artificial Intelligence in Heart Failure with Preserved Ejection Fraction.Diagnostics (Basel, Switzerland) · 2026Review
- Current concepts and challenges in pediatric diastolic dysfunction and heart failure with preserved ejection fraction.Pediatric research · 2026Review
- Artificial Intelligence-Enabled ECG for Elevated E/e' on Echocardiography: Hemodynamic Relevance and Prognostic Value.Journal of the American Heart Association · 2026Article
- Personalized artificial intelligence based left ventricular ejection fraction and systolic dysfunction assessment.NPJ digital medicine · 2026Article
- Integrating multimodal intelligence in heart failure: AI-driven risk prediction, precision diagnosis, phenotyping, personalized treatment, and prognosis.Chinese medical journal · 2026Review
- Deep learning algorithm for detection of acute heart failure using standard ECG waveforms.European heart journal. Digital health · 2026Article
- Large language model detects previously undiagnosed heart failure with preserved ejection fraction in patients with metabolic-associated fatty liver disease: A multicenter cohort study.PLOS digital health · 2026Article
- Risk Stratification and Outcome Prediction in Heart Failure Patients With Cardiac Implantable Electronic Devices Using Machine Learning Analysis From the HINODE Study.Circulation reports · 2026Article
- Web-Based Application of Simplified Machine Learning for Detecting Reduced LVEF From 12-Lead ECG.Journal of arrhythmia · 2026Article
- 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
- The Narrative Review: Advancements in Heart Failure Diagnosis and Management using Artificial Intelligence: A New Era of Patient Care.Current cardiology reviews · 2026Review
- ECG-based deep learning for chronic kidney disease detection and cardiovascular risk prediction.BMC medical informatics and decision making · 2025Article
- Atrial cardiomyopathy: From healthy atria to atrial failure. A clinical consensus statement of the Heart Failure Association of the ESC.European journal of heart failure · 2025Article
Corrections and comments
- Erratum issuedErratum.2022
Authors and funding
8 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aims: Although heart failure with preserved ejection fraction (HFpEF) is a rapidly emerging global health problem, an adequate tool to screen it reliably and economically does not exist. We developed an interpretable deep learning model (DLM) using electrocardiography (ECG) and validated its performance. Methods and results: This retrospective cohort study included two hospitals. 34 103 patients who underwent echocardiography and ECG within 1 week and indicated normal left ventricular systolic function were included in this study. A DLM based on an ensemble neural network was developed using 32 671 ECGs of 20 169 patients. The internal validation included 1979 ECGs of 1979 patients. Furthermore, we conducted an external validation with 11 955 ECGs of 11 955 patients from another hospital. The endpoint was to detect HFpEF. During the internal and external validation, the area under the receiver operating characteristic curves of a DLM using 12-lead ECG for detecting HFpEF were 0.866 (95% confidence interval 0.850-0.883) and 0.869 (0.860-0.877), respectively. In the 1412 individuals without HFpEF at initial echocardiography, patients whose DLM was defined as having a higher risk had a significantly higher chance of developing HFpEF than those in the low-risk group (33.6% vs. 8.4%, Conclusion: The DLM demonstrated high performance for HFpEF detection using not only a 12-lead ECG but also 6- single-lead ECG. These results suggest that HFpEF can be screened using conventional ECG devices and diverse life-type ECG machines employing the DLM, thereby preventing disease progression.
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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.