ArticleJournal of the American Heart Association2026
Artificial Intelligence-Enabled ECG for Elevated E/e' on Echocardiography: Hemodynamic Relevance and Prognostic Value.
Article in Journal of the American Heart Association, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- 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
- Serial AI-Enabled Electrocardiogram Trajectories During Heart Failure Treatment: A Proof-of-Concept Case Series.JACC. Case reports · 2026Article
- Interpreting Serial AI-ECG Changes During Heart Failure Therapy: From Disease Detection to Disease Tracking.JACC. Case reports · 2026Article
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11 authors.
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Abstract
backgroundLeft ventricular filling pressure is associated with heart failure symptoms and a key prognostic marker and therapeutic target, but a scalable, accessible, and affordable tool for its noninvasive, serial estimation remains lacking. We developed an artificial intelligence (AI) model using a standard 12-lead ECG to detect increased E/e', a general surrogate of elevated left ventricular filling pressure on echocardiography.
methodsThe AI model was built upon a foundation model trained with >1 million multiethnic ECGs and fine-tuned through a development cohort of 225 737 ECGs and 115 982 echocardiogram records from 92 775 unique patients across 2 tertiary hospitals. The model performance was assessed in a separate internal test set (n=9278) and an independent external cohort from another tertiary hospital (n=17 926). Prognostic significance of the AI-ECG output was evaluated in these hospital cohorts, as well as the UK Biobank (n=43 347). Finally, we validated the model output against invasively measured left ventricular filling pressure through cardiac catheterization (n=60).
resultsThe AI-ECG model detected increased E/e' with an area under the curve of 0.868 (95% CI, 0.859-0.877) and 0.850 (95% CI, 0.841-0.858) in the internal and external test cohorts, respectively. The AI-ECG output value demonstrated a strong correlation with invasively measured left ventricular end-diastolic pressure (Pearson's r=0.655) and was significantly associated with incident heart failure and mortality.
conclusionsThe AI-ECG may enable identification of patients with increased left ventricular filling pressure and provide powerful prognostic information. Further prospective studies are warranted to evaluate its clinical utility.
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