ArticleNPJ digital medicine2024
Artificial intelligence-enabled ECG for left ventricular diastolic function and filling pressure.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers.
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Who cites it
57 citing papers in PubMed, 75 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
- Associations between echocardiographic traits and artificial intelligence-enabled electrocardiography predictions of heart failure.European heart journal. Digital health · 2026Article
- Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease.Diagnostics (Basel, Switzerland) · 2026Review
- Association of isolated anti-Scl-70 positivity with subclinical cardiac involvement in ANA-positive individuals: an electrocardiographic diastolic function analysis (Subclinical Cardiac Involvement in Isolated Anti-Scl-70 Positivity).Immunologic research · 2026Article
- Understanding HFpEF Through Imaging: Strengths, Limitations, and Current State of the Art.JACC. Cardiovascular imaging · 2026Review
- Differentiating HFmr/rEF from HFpEF using standard 12-lead ECG measurements: an interpretable machine learning study.Open heart · 2026Article
- Artificial Intelligence-Enabled Serial Electrocardiograms for Prediction of All-Cause Mortality in Secondary Care Settings.JACC. Advances · 2026Article
- Artificial intelligence electrocardiography for left ventricular systolic dysfunction demonstrates preserved performance across demographic training imbalances.European heart journal. Digital health · 2026Article
- Artificial Intelligence in Heart Failure with Preserved Ejection Fraction.Diagnostics (Basel, Switzerland) · 2026Review
- Heart failure detection in electrocardiograms using artificial intelligence and pragmatic labelling.NPJ digital medicine · 2026Article
- Incremental Prognostic Power of Coronary Flow Reserve and Artificial Intelligence-Enabled ECG-Derived Filling Pressure in Angina With Nonobstructive Coronary Arteries.Journal of the American Heart Association · 2026Article
- Artificial Intelligence-Enabled ECG for Elevated E/e' on Echocardiography: Hemodynamic Relevance and Prognostic Value.Journal of the American Heart Association · 2026Article
- Artificial intelligence in cardiology in the current era: a narrative review.Cardiovascular diagnosis and therapy · 2026Review
- Revolutionizing Heart Failure Management With Artificial Intelligence: A Narrative Review of Diagnostic, Prognostic, and Therapeutic Innovations.Health science reports · 2026Article
- Echocardiographic Diastolic Function Grading in HFpEF: Testing the Updated 2025 ASE Criteria.Journal of the American College of Cardiology · 2026Article
- Automated Classification of Mitral and Tricuspid Regurgitation With Explainability and Real-World Practice Experience.Journal of the American Heart Association · 2026Article
- Integrating multimodal intelligence in heart failure: AI-driven risk prediction, precision diagnosis, phenotyping, personalized treatment, and prognosis.Chinese medical journal · 2026Review
- Artificial intelligence in heart failure.The Egyptian heart journal : (EHJ) : official bulletin of the Egyptian Society of Cardiology · 2026Review
- Artificial Intelligence-Enabled Electrocardiography in Practice: A State-of-the-Art Review.Korean circulation journal · 2026Review
- Prognostic Value of Artificial Intelligence-Enabled Electrocardiography-Derived Diastolic Dysfunction Grading and Trajectory in Patients Undergoing Transcatheter Aortic Valve Replacement.Journal of the American Heart Association · 2026Article
Corrections and comments
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Authors and funding
12 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Assessment of left ventricular diastolic function plays a major role in the diagnosis and prognosis of cardiac diseases, including heart failure with preserved ejection fraction. We aimed to develop an artificial intelligence (AI)-enabled electrocardiogram (ECG) model to identify echocardiographically determined diastolic dysfunction and increased filling pressure. We trained, validated, and tested an AI-enabled ECG in 98,736, 21,963, and 98,763 patients, respectively, who had an ECG and echocardiographic diastolic function assessment within 14 days with no exclusion criteria. It was also tested in 55,248 patients with indeterminate diastolic function by echocardiography. The model was evaluated using the area under the curve (AUC) of the receiver operating characteristic curve, and its prognostic performance was compared to echocardiography. The AUC for detecting increased filling pressure was 0.911. The AUCs to identify diastolic dysfunction grades ≥1, ≥2, and 3 were 0.847, 0.911, and 0.943, respectively. During a median follow-up of 5.9 years, 20,223 (20.5%) died. Patients with increased filling pressure predicted by AI-ECG had higher mortality than those with normal filling pressure, after adjusting for age, sex, and comorbidities in the test group (hazard ratio (HR) 1.7, 95% CI 1.645-1.757) similar to echocardiography and in the indeterminate group (HR 1.34, 95% CI 1.298-1.383). An AI-enabled ECG identifies increased filling pressure and diastolic function grades with a good prognostic value similar to echocardiography. AI-ECG is a simple and promising tool to enhance the detection of diseases associated with diastolic dysfunction and increased diastolic filling pressure.
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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.