Evidence map›Paper›PMID 42668860›Full record

ArticleEuropean heart journal. Digital health2026

Artificial intelligence-enhanced electrocardiography for the prediction of future type 2 diabetes mellitus: a model-development and multicentre validation study.

Libor Pastika, Konstantinos Patlatzoglou, Ewa Sieliwonczyk, Joseph Barker, Boroumand Zeidaabadi, Kathryn A McGurk, Sandhi M Barreto, Lidyane Camelo, Sadia Khan, William R Scott and 11 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in European heart journal. Digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

21 authors.

Libor PastikaNational Heart and Lung Institute, Imperial College London, Hammersmith Campus, Du Cane Road, London W12 0NN, UK.ORCID https://orcid.org/0000-0001-6892-6553
Konstantinos PatlatzoglouNational Heart and Lung Institute, Imperial College London, Hammersmith Campus, Du Cane Road, London W12 0NN, UK.ORCID https://orcid.org/0000-0002-5888-8490
Ewa SieliwonczykNational Heart and Lung Institute, Imperial College London, Hammersmith Campus, Du Cane Road, London W12 0NN, UK.ORCID https://orcid.org/0000-0002-8603-7044
Joseph BarkerNational Heart and Lung Institute, Imperial College London, Hammersmith Campus, Du Cane Road, London W12 0NN, UK.ORCID https://orcid.org/0000-0002-5483-9608
Boroumand ZeidaabadiNational Heart and Lung Institute, Imperial College London, Hammersmith Campus, Du Cane Road, London W12 0NN, UK.
Kathryn A McGurkNational Heart and Lung Institute, Imperial College London, Hammersmith Campus, Du Cane Road, London W12 0NN, UK.
Sandhi M BarretoDepartment of Preventive Medicine, School of Medicine & Hospital das Clínicas/Empresa Brasileira de Serviços Hospitalares, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.ORCID https://orcid.org/0000-0001-7383-7811
Lidyane CameloDepartment of Preventive Medicine, School of Medicine & Hospital das Clínicas/Empresa Brasileira de Serviços Hospitalares, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.
Sadia KhanNational Heart and Lung Institute, Imperial College London, Hammersmith Campus, Du Cane Road, London W12 0NN, UK.
William R ScottMRC Laboratory of Medical Sciences, Imperial College London, London, UK.
Declan P O'ReganMRC Laboratory of Medical Sciences, Imperial College London, London, UK.
Bruce B DuncanPostgraduate Program in Epidemiology and Hospital de Clínicas de Porto Alegre, Federal University of Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil.ORCID https://orcid.org/0000-0002-7491-2630
Maria I SchmidtPostgraduate Program in Epidemiology and Hospital de Clínicas de Porto Alegre, Federal University of Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil.ORCID https://orcid.org/0000-0002-3837-0731
James S WareNational Heart and Lung Institute, Imperial College London, Hammersmith Campus, Du Cane Road, London W12 0NN, UK.
Shivani MisraDivision of Metabolism, Digestion & Reproduction, Imperial College London, London, UK.
Daniel B KramerNational Heart and Lung Institute, Imperial College London, Hammersmith Campus, Du Cane Road, London W12 0NN, UK.
Jonathan W WaksHarvard-Thorndike Electrophysiology Institute, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
Nicholas S PetersNational Heart and Lung Institute, Imperial College London, Hammersmith Campus, Du Cane Road, London W12 0NN, UK.ORCID https://orcid.org/0000-0002-3581-8078
Antonio Luiz Pinho RibeiroDepartment of Internal Medicine, Faculdade de Medicina, and Telehealth Center and Cardiology Service, Hospital das Clínicas (A.L.P.R.), Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.
Arunashis SauNational Heart and Lung Institute, Imperial College London, Hammersmith Campus, Du Cane Road, London W12 0NN, UK.
Fu Siong NgNational Heart and Lung Institute, Imperial College London, Hammersmith Campus, Du Cane Road, London W12 0NN, UK.ORCID https://orcid.org/0000-0002-8681-4368

Funding

Wellcome Trust
6 · The paper itself

Abstract

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; Conclusion: AI-ECG Risk Estimator for Diabetes Mellitus detects prevalent type 2 diabetes and predicts incident disease, uniquely identifying high-risk individuals within the normoglycaemic range. Combined with clinical scores or biomarkers, it enhances risk stratification, enabling earlier intervention.

Indexed as

AI-ECGAI-enhanced ECGAIRE platformArtificial intelligence (AI)Diabetes predictionElectrocardiography (ECG)Pre-diabetesRisk stratificationType 2 diabetes mellitus (T2DM)

Identifiers

PMID42668860
PMCPMC13525370

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.