  Undiagnosed diabetes is present in 21.4% of adults with diabetes. Diabetes
can remain asymptomatic and undetected due to limitations in screening rates.
To address this issue, questionnaires, such as the American Diabetes
Association (ADA) Risk test, have been recommended for use by physicians and
the public. Based on evidence that blood glucose concentration can affect
cardiac electrophysiology, we hypothesized that an artificial intelligence
(AI)-enhanced electrocardiogram (ECG) could identify adults with new-onset
diabetes. We trained a neural network to estimate HbA1c using a 12-lead ECG and
readily available demographics. We retrospectively assembled a dataset
comprised of patients with paired ECG and HbA1c data. The population of
patients who receive both an ECG and HbA1c may a biased sample of the complete
outpatient population, so we adjusted the importance placed on each patient to
generate a more representative pseudo-population. We found ECG-based assessment
outperforms the ADA Risk test, achieving a higher area under the curve (0.80
vs. 0.68) and positive predictive value (13% vs. 9%) -- 2.6 times the
prevalence of diabetes in the cohort. The AI-enhanced ECG significantly
outperforms electrophysiologist interpretation of the ECG, suggesting that the
task is beyond current clinical capabilities. Given the prevalence of ECGs in
clinics and via wearable devices, such a tool would make precise, automated
diabetes assessment widely accessible.
