Key result
Serial AI-ECG scores broadly track echo and biomarker changes during HF treatment.
Why the study?
AI-ECG detects left ventricular systolic and diastolic dysfunction at single time points, but longitudinal behavior during heart failure treatment remains unexplored.
Does serial AI-ECG analysis track echocardiographic and biochemical trajectories in patients undergoing heart failure treatment?
Case Report (n=4)
Does serial AI-ECG analysis track echocardiographic and biochemical trajectories in patients undergoing heart failure treatment?
Serial AI-ECG scores show feasibility in tracking longitudinal echocardiographic and biochemical changes during heart failure treatment, though rhythm and pacing changes can confound results.
Answers are grounded in this paper and cite their sources.
May support serial AI-ECG for tracking HF response; hypothesis-generating from case series and requires prospective validation.
BACKGROUND: Artificial 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. METHODS: In 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. RESULTS: Serial 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. CONCLUSIONS: These 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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Yoo et al. (2026) conducted a case report in Heart failure (n=4). Serial AI-Enabled Electrocardiogram (AI-ECG) analysis was evaluated on Concordance of serial AI-ECG scores with echocardiographic and biochemical trajectories. Serial AI-ECG scores were broadly directionally concordant with echocardiographic and biochemical changes during heart failure treatment, despite some rhythm-related discordances.
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