Artificial Intelligence Analysis of Standard 12-Lead Electrocardiograms for Screening of Symptomatic Diabetic...

To investigate whether artificial intelligence (AI) models trained on standard 12-lead electrocardiograms (ECG) can identify symptom-defined diabetic peripheral neuropathy (DPN) as assessed by the Michigan Neuropathy Screening Instrument Questionnaire (MNSI-Q).

This was an observational study of people with diabetes enrolled in the Silesia-Diabetes Heart Project. DPN was assessed using the MNSI-Q. We used an original questionnaire cut-off score of ≥ 7 and revised cut-off score of ≥ 4 to diagnose DPN and classified DPN as highly symptomatic and moderately symptomatic accordingly. Feature extraction from 10-s raw ECG recordings utilised algorithms to identify recurring signal segments (motifs) and anomalies (discords). These features were used to train XGBoost, Support Vector Machine (SVM) and Ridge classifiers to differentiate between DPN-positive and DPN-negative people.

A total of 640 participants (mean age 54 ± 17; 52% female) were included. Of these, 95 (15%) had highly symptomatic DPN (MNSI-Q ≥ 7) and 281 (44%) had moderately symptomatic DPN (MNSI-Q ≥ 4). For the highly symptomatic DPN, the XGBoost classifier utilizing a combination of motifs and discords demonstrated the highest performance, achieving an area under the receiver-operating characteristic curve (AUC) of 0.89 (95% CI 0.88-0.91), an accuracy of 88.5% and a sensitivity of 93.4%. The model showed substantially lower predictive capability when tested against the broader screening threshold of MNSI ≥ 4 (AUC 0.64).

AI analysis of the standard ECG demonstrated a strong association with the presence of symptom-defined peripheral neuropathy but lacked sensitivity for milder presentation. With further validation, this method could serve as an accessible, supplementary screening aid to help identify high-risk patients during routine cardiovascular assessment.