Key result
Synthesized 20° single-lead ECG AI models outperform standard Lead I for detecting LVSD with ~0.90 AUROC.
Why the study?
The optimal single-lead ECG axis for detecting myocardial diseases beyond rhythm abnormalities using artificial intelligence remains unclear.
What is the optimal single-lead ECG axis for AI-based detection of myocardial diseases?
Observational
Yes
What is the optimal single-lead ECG axis for AI-based detection of myocardial diseases?
Absolute Event Rate: 0.902% vs 0.887%
p-value: p=<0.05
The aVR axis and its inverse (20° and 210°) provide the highest discrimination for AI-based detection of LVSD, HCM, and CA using single-lead ECGs, which can inform the design of future wearable devices.
May facilitate LVSD detection at optimized angles; leaves open prospective validation before clinical adoption.
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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Nakayama et al. (2026) conducted an observational in Myocardial diseases (Left ventricular systolic dysfunction, Hypertrophic cardiomyopathy, Cardiac amyloidosis). Synthesized single-lead ECG at optimal axis (20°) vs. Standard Lead I (0°) ECG was evaluated on Detection of left ventricular systolic dysfunction (AUROC of integrated model) (95% CI 0.889-0.916, p=<0.05). AI models trained on synthesized single-lead ECGs at 20° achieved a significantly higher AUROC of 0.902 for detecting left ventricular systolic dysfunction compared to 0.887 for standard Lead I.
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