ArticleNPJ cardiovascular health2026
Optimizing single-lead ECG axis for AI-based detection of myocardial diseases.
Article in NPJ cardiovascular health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Wearable devices enable electrocardiograms (ECGs) outside traditional healthcare settings. While these devices are usually equipped with single-lead ECGs to monitor rhythm abnormalities, recent studies show utility in detecting myocardial diseases when combined with artificial intelligence (AI). However, the optimal single-lead axis for detecting myocardial diseases beyond rhythm abnormality remains unclear. To address this knowledge gap, we trained models to detect 3 myocardial diseases: left ventricular systolic dysfunction (LVSD), hypertrophic cardiomyopathy (HCM), and cardiac amyloidosis (CA) using single-lead ECGs, systematically synthesized at 10-degree intervals, leveraging the limb leads from 12-lead ECG data. For all 3 diseases, the highest discriminations were observed near the aVR axis and its inverse. The optimal angles were 20° for LVSD (AUROC 0.884 [95% CI: 0.877-0.891]) and HCM (0.911 [0.900-0.922]), and 210° for CA (0.893 [0.888-0.899]). These findings may inform the development of wearable ECG devices with utility beyond rhythm abnormality detection.
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