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.

翻译：未确诊糖尿病占糖尿病患者总数的21.4%。由于筛查率的限制，糖尿病可能保持无症状且未被发现。为解决这一问题，美国糖尿病协会（ADA）风险测试等问卷已被推荐供医生和公众使用。基于血糖浓度可影响心脏电生理学的证据，我们假设人工智能（AI）增强心电图（ECG）能够识别初发糖尿病成人患者。我们训练了一个神经网络，利用12导联心电图及易于获取的人口学数据来估算糖化血红蛋白（HbA1c）。我们回顾性地构建了一个包含配对心电图与糖化血红蛋白数据的患者数据集。同时接受心电图和糖化血红蛋白检查的患者群体可能是全部门诊患者中的偏倚样本，因此我们调整了每位患者的重要性权重，以生成更具代表性的人群。我们发现基于心电图的评估优于ADA风险测试，获得了更高的曲线下面积（0.80对0.68）和阳性预测值（13%对9%）——该值是该队列糖尿病患病率的2.6倍。AI增强心电图显著优于电生理专家对心电图的解读，表明该任务超出了当前临床能力范畴。鉴于心电图在诊所及可穿戴设备中的普及性，此类工具将使精准、自动化的糖尿病评估得以广泛普及。