ArticleJACC. Case reports2026
Serial AI-Enabled Electrocardiogram Trajectories During Heart Failure Treatment: A Proof-of-Concept Case Series.
Article in JACC. Case reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 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
- Interpreting Serial AI-ECG Changes During Heart Failure Therapy: From Disease Detection to Disease Tracking.JACC. Case reports · 2026Article
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Authors and funding
3 authors.
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Abstract
backgroundArtificial intelligence-enabled electrocardiogram (AI-ECG) detects left ventricular systolic and diastolic dysfunction at single time points, but longitudinal behavior during heart failure treatment remains unexplored. We examined whether paired serial AI-ECG scores track echocardiographic and biochemical trajectories.
methodsIn this retrospective 4-patient case series, previously validated deep learning AI-ECG models generated left ventricular systolic dysfunction and left ventricular diastolic dysfunction scores from serial 12-lead ECGs, paired with ejection fraction, E/e', and N-terminal pro-B-type natriuretic peptide across treatment courses spanning revascularization, valve replacement with resynchronization therapy, percutaneous coronary intervention, and rhythm control.
resultsSerial score changes were broadly directionally concordant with echocardiographic and biochemical changes. Case-level and within-case discordances occurred, including persistent left ventricular systolic dysfunction elevation after resynchronization and bidirectional movement coinciding with rhythm transitions-consistent with rhythm- and pacing-related effects on ECG morphology.
conclusionsThese hypothesis-generating observations support the feasibility of longitudinal paired AI-ECG analysis and highlight the need for rhythm- and confounder-aware design in future studies.
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