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 (14% 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.

翻译：21.4%的患有糖尿病的成年人患有未经诊断的糖尿病。糖尿病可以保持无症状和不被发现,因为筛查率有限。为解决这一问题,医生和公众建议使用诸如美国糖尿病协会风险测试等问卷,供医生和公众使用。根据血糖浓度可能影响心脏电电生理学的证据,我们假设人工智能(AI)增强型心电图(ECG)可以识别患有新发糖尿病的成年人。我们训练了一个神经网络,利用12个领先的ECG和随时可用的准确人口统计来估计HbA1c。我们追溯地收集了由配对ECG和HbA1c数据的病人组成的数据集。接受ECG和HbA1c的病人人口可能会对全部门诊人口进行偏差的抽样,因此我们调整了对每个病人的重视度以产生更具代表性的假人口。我们发现基于ECG的评估超越了AD风险测试的可理解性能,在直线(0.80比ECG)下实现更高的区域,在目前对ECG值值值值值值(14.80比G)中,对AI值做出肯定的预测。