Letter to the Editor: Do we need an artificial intelligence-assisted electrocardiographic tool to diagnose diabetes mellitus or to predict its unseen cardiovascular consequences?

Table 1 Examples of artificial intelligence-assisted electrocardiogram for detection of diabetes mellitus

Ref.	Technique	Comment
Zhang et al[19]	Training deep learning models for clinical screening from retrospective data presents a fundamental challenge: Our data contains only patients where both the ECG and HbA1c were measured	AI-enabled single-lead ECG (lead I), retains much of the discriminative performance (AUC, 0.78) and could be utilized to automate the identification of patients who are likely to have DM
Koga et al[20]	SHAP was applied to assess feature importance and clinical interpretability	A LightGBM-based algorithm (DiaCardia), achieved an AUR of 0.851. It can accurately identify subjects with prediabetes from an ECG alone, with performance that is robust across different patient cohorts and independent of major clinical confounders. A version of DiaCardia using only single-lead (lead I) ECG data achieved a comparable AUROC
Kulkarni et al[21]	1D convolutional neural network (CNN)	The model diagnosed DM from a single-lead ECG with 90.3% accuracy, 92.1% sensitivity, and 88.6% specificity. It identified ECG intervals (QT, QTc) as key biomarkers
Gragnaniello et al[22]	Dimensional Convolutional Neural Network (1D-CNN) to analyze the ECG signals directly on the device	The final model achieves an accuracy of 89.52%, with an average precision and recall of 0.91 and 0.90, respectively
Yildirim et al[23]	The one-dimensional hear rate signals were converted into frequency spectrum images, which were utilized for application to well-known pre-trained models, specifically: AlexNet, VggNet, ResNet, and DenseNet	DenseNet pre-trained model yielded the highest classification average accuracy of 97.62%, and sensitivity of 100%, to detect DM subjects via hear rate signal recordings
Cordeiro et al[24]	A deep learning architecture that can identify hyperglycemia using heartbeats from ECG signals	The high sensitivity and specificity achieved by 10-layer deep neural network showed that ECG possesses intrinsic information that can indicate the level of blood glucose concentration

1D: Single dimension of the input data; SHAP: SHapley Additive exPlanations; ECG: Electrocardiogram; AUC: Area under the curve; DM: Diabetes mellitus; HbA1c: Glycated hemoglobin; AUROC: Area under the receiver operating characteristic; AI: Artificial intelligence.