Artificial intelligence-enabled detection of left ventricular hypertrophy using a single-lead electrocardiogram: a pilot study for wearable-based screening.

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.