BibTex format
@article{Pastika:2026:ehjdh/ztag118,
author = {Pastika, L and Patlatzoglou, K and Sieliwonczyk, E and Barker, J and Zeidaabadi, B and McGurk, KA and Barreto, SM and Camelo, L and Khan, S and Scott, WR and O'Regan, DP and Duncan, BB and Schmidt, MI and Ware, JS and Misra, S and Kramer, DB and Waks, JW and Peters, NS and Ribeiro, ALP and Sau, A and Ng, FS},
doi = {ehjdh/ztag118},
journal = {Eur Heart J Digit Health},
title = {Artificial intelligence-enhanced electrocardiography for the prediction of future type 2 diabetes mellitus: a model-development and multicentre validation study.},
url = {http://dx.doi.org/10.1093/ehjdh/ztag118},
volume = {7},
year = {2026}
}
RIS format (EndNote, RefMan)
TY - JOUR
AB - AIMS: A significant proportion of type 2 diabetes cases remain undiagnosed despite screening advances, carrying substantial cardiometabolic risk. Artificial intelligence-enhanced electrocardiography (AI-ECG) detects subtle ECG changes in subclinical disease, potentially enabling opportunistic screening. METHODS AND RESULTS: We developed AI-ECG Risk Estimator for Diabetes Mellitus (AIRE-DM), a convolutional neural network with discrete-time survival loss, for diagnosis of prevalent and prediction of incident type 2 diabetes. It was trained on 1 163 401 ECGs from 189 537 individuals from Beth Israel Deaconess Medical Center (BIDMC) and externally validated in UK Biobank (UKB; n = 65 606) and ELSA-Brasil (n = 13 739). AI-ECG Risk Estimator for Diabetes Mellitus demonstrated moderate discrimination for prevalent type 2 diabetes (area under the receiver operating characteristic curve: BIDMC 0.724, UKB 0.733, ELSA-Brasil 0.706) and incident type 2 diabetes (C-index: BIDMC 0.667, UKB 0.688, ELSA-Brasil 0.625). The highest AIRE-DM risk quartile had elevated incident diabetes risk vs. the lowest (hazard ratio: BIDMC 4.75, UKB 7.52, ELSA-Brasil 3.96). AI-ECG Risk Estimator for Diabetes Mellitus was non-inferior to the American Diabetes Association Diabetes Risk Test in BIDMC, with improved predictive accuracy when combined. In normoglycaemic patients, AIRE-DM was superior to glycated haemoglobin (HbA1c) for predicting incident diabetes in BIDMC and non-inferior in ELSA-Brasil. The highest risk quartile reached 5% cumulative type 2 diabetes mellitus incidence 5.4 years (BIDMC) and 4.8 years (ELSA-Brasil) earlier than the lowest risk quartile, after adjusting for HbA1c, age, and sex. Phenome- and genome-wide association studies revealed biologically plausible associations with glucose regulation, cardiac morphology, diastolic dysfunction, arterial stiffness, and lipid metabolism. CONCLUSION: AI-ECG Risk Estimator for Diabetes Mellitus detects prevalent type 2 diabetes and predi
AU - Pastika,L
AU - Patlatzoglou,K
AU - Sieliwonczyk,E
AU - Barker,J
AU - Zeidaabadi,B
AU - McGurk,KA
AU - Barreto,SM
AU - Camelo,L
AU - Khan,S
AU - Scott,WR
AU - O'Regan,DP
AU - Duncan,BB
AU - Schmidt,MI
AU - Ware,JS
AU - Misra,S
AU - Kramer,DB
AU - Waks,JW
AU - Peters,NS
AU - Ribeiro,ALP
AU - Sau,A
AU - Ng,FS
DO - ehjdh/ztag118
PY - 2026///
TI - Artificial intelligence-enhanced electrocardiography for the prediction of future type 2 diabetes mellitus: a model-development and multicentre validation study.
T2 - Eur Heart J Digit Health
UR - http://dx.doi.org/10.1093/ehjdh/ztag118
UR - https://www.ncbi.nlm.nih.gov/pubmed/42668860
VL - 7
ER -