Key result
Single-lead ECG deep learning detects LVH with ~0.93 AUC, approaching 12-lead model performance.
Why the study?
Does an AI model using single-lead ECG plus demographics accurately detect left ventricular hypertrophy compared to a 12-lead ECG model?
Observational (n=18,885)
Does an AI model using single-lead ECG plus demographics accurately detect left ventricular hypertrophy compared to a 12-lead ECG model?
Absolute Event Rate: 0.928% vs 0.939%
A deep learning model using single-lead ECG and basic demographics can detect left ventricular hypertrophy with performance approaching that of a standard 12-lead ECG, suggesting potential for wearable-based screening.
Single-lead wearable ECG with AI may enable scalable LVH screening; leaves open prospective validation before clinical adoption.
BACKGROUND: Left ventricular hypertrophy (LVH) is an independent predictor of cardiovascular morbidity and mortality. Single-lead electrocardiogram (ECG) tracings from consumer wearable devices may enable scalable screening for LVH where standard 12-lead ECG acquisition is not feasible. METHODS: We developed a transformer-based deep learning model to detect LVH using single-lead ECG (lead I) combined with demographic variables and benchmarked performance against a 12-lead ECG model within the same framework. The PTB-XL dataset (21,837 cardiologist-annotated ECGs from 18,885 patients) was used. Models were trained on (a) lead I only, (b) lead I plus demographics (age, sex, body mass index), and (c) 12-lead plus demographics. Classification thresholds were selected to achieve ≥90% specificity consistent with a rule-in screening paradigm. RESULTS: The lead I-only model achieved an area under the curve (AUC) of 0.874 ± 0.062 (sensitivity 69.1% ± 18.9%, specificity 90.1% ± 0.1%). Incorporation of demographic variables improved performance to AUC 0.928 ± 0.017 (sensitivity 84.7% ± 4.2%, specificity 90.6% ± 0.7%). The 12-lead model with demographics achieved AUC 0.939 ± 0.007 (sensitivity 85.9% ± 2.0%, specificity 90.1% ± 0.1%). CONCLUSIONS: Single-lead ECG combined with demographic variables enables detection of electrocardiographic LVH with performance approaching that of 12-lead ECG models, supporting the feasibility of wearable-based screening pending validation against imaging-confirmed LVH.
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Nandy et al. (2026) conducted an observational in Left ventricular hypertrophy (n=18,885). Single-lead ECG (lead I) combined with demographic variables vs. 12-lead ECG combined with demographic variables was evaluated on Detection of left ventricular hypertrophy (AUC). A deep learning model using single-lead ECG and demographics detected left ventricular hypertrophy with an AUC of 0.928, approaching the performance of a 12-lead ECG model (AUC 0.939).
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