Wearable and handheld devices record a Lead I-like electrocardiogram (ECG), but it is unclear whether a single lead retains the information that deep learning extracts from the 12-lead ECG for coronary disease. We developed two one-dimensional residual network models, oneusingthe12-leadECGandonerestrictedtoLeadI,withidenticalpreprocessing, patient-level partitions and five-fold cross-validation, to detect coded coronary artery disease/coronary heart disease (CAD/CHD). The cohort comprised the most recent ECG of 161,306 adults in MIMIC-IV-ECG linked to MIMIC-IV, a hospital-based population spanning emergency, inpatient (including intensive care) and outpatient settings, in which controls included patients with other cardiovascular disease. In a shared holdout cohort of 31,497 patients (943 cases), the area under the receiver operating characteristic curve (AUC) was 0.764 (95% CI 0.751-0.777) for the 12-lead model and 0.744 (0.730-0.759) for Lead I (p < 0.001). Predictions were strongly correlated (Pearson r = 0.78), binary classifications agreed in 81.8% of patients, and sensitivity did not differ between models (75.0% vs. 74.0%, p = 0.55). Both models also discriminated heart failure with reduced ejection fraction, including after excluding CAD/CHD. Grad-CAM showed that both models shifted attribution towards the ST segment in ECGs classified as positive, although their attribution profiles were only partly shared. Adding the Lead I prediction to age, sex and race/ethnicity increased the AUC from 0.734 to 0.773. A single Lead I ECG retained most, but not all, of the CAD/CHD-related information available to the 12-lead model.