Key result
AI-enhanced ECG models differentiate HCM subtypes with AUROCs >0.92 using only ten parameters.
Why the study?
Does AI-enhanced ECG parameter profiling accurately differentiate hypertrophic cardiomyopathy stages?
Observational
Does AI-enhanced ECG parameter profiling accurately differentiate hypertrophic cardiomyopathy stages?
AI-enhanced ECG models using specific parameter profiles can accurately differentiate between stages of hypertrophic cardiomyopathy, including its dilated phase.
Background: The efficacy of artificial intelligence (AI)-enhanced electrocardiography (ECG) for detecting hypertrophic cardiomyopathy (HCM) and its dilated phase (dHCM) has been developed, though specific ECG characteristics associated with these conditions remain insufficiently characterized. Methods: in univariate logistic regression, while multivariate logistic regression was used to determine the area under the receiver operating characteristic curves (AUROC). Results: In HCM-basal and HCM-apical, HPI was predominantly observed in the ST-T segment (49% and 51%, respectively), followed by the QRS complex (29% and 27%). For dHCM, HPI was lower in the ST-T segment (16%) and QRS complex (22%). The P-wave had low HPI across all subtypes. AUROCs for models with total ECG parameters were 0.925 for HCM-basal, 0.981 for HCM-apical, and 0.969 for dHCM. While AUROCs for the top 10 HPI models were lower than the total ECG parameter model for HCM total, they were comparable across HCM subtypes. Conclusions: As HCM progresses to dHCM, a shift in HPI from the ST-T segment to the QRS complex provides clinically relevant insights. For HCM subtypes, the top 10 ECG parameters yield predictive performance similar to the full parameter set, supporting efficient approaches for AI-based diagnostic models.
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Hirota et al. (2025) conducted an observational in Hypertrophic cardiomyopathy (HCM) and its dilated phase (dHCM). AI-enhanced electrocardiography (ECG) parameter profiling was evaluated on Area under the receiver operating characteristic curves (AUROC) for detecting HCM subtypes. AI-enhanced ECG models using total parameters achieved high AUROCs for differentiating HCM-basal (0.925), HCM-apical (0.981), and dHCM (0.969), with top 10 parameters yielding similar performance.
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