Evidence map›Paper›PMID 42129209›Full record

ArticleNature communications2026

A deep learning ECG model for identification and localization of occlusion myocardial infarction.

Stefan Gustafsson, Antônio H Ribeiro, Daniel Gedon, Petrus E O G B Abreu, Nicolas Pielawski, Gabriela M M Paixão, Marco Antonio Gutierrez, José Eduardo Krieger, Felipe Meneguitti Dias, Antonio Luiz P Ribeiro and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Clinical cognition in the age of cardiovascular AI.Frontiers in cardiovascular medicine · 2026
    Article
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

13 authors.

Stefan GustafssonDepartment of Medical Sciences, Clinical Epidemiology Unit, Uppsala University, Uppsala, Sweden.ORCID http://orcid.org/0000-0001-5894-0351
Antônio H RibeiroDepartment of Information Technology, Division of Systems and Control, Uppsala University, Uppsala, Sweden.ORCID http://orcid.org/0000-0003-3632-8529
Daniel GedonMachine Learning in Science, University of Tübingen, Tübingen, Germany.
Petrus E O G B AbreuPostgraduate Program in Health Sciences: Infectious Diseases and Tropical Medicine, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.
Nicolas PielawskiDepartment of Information Technology, Division of Systems and Control, Uppsala University, Uppsala, Sweden.
Gabriela M M PaixãoDepartment of Internal Medicine, Faculdade de Medicina, and Telehealth Center and Cardiology Service, Hospital das Clínicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.
Marco Antonio GutierrezHeart Institute, University of São Paulo Medical School, São Paulo, Brazil.ORCID http://orcid.org/0000-0003-0964-6222
José Eduardo KriegerHeart Institute, University of São Paulo Medical School, São Paulo, Brazil.ORCID http://orcid.org/0000-0001-5464-1792
Felipe Meneguitti DiasHeart Institute, University of São Paulo Medical School, São Paulo, Brazil.ORCID http://orcid.org/0000-0001-7778-4606
Antonio Luiz P RibeiroDepartment of Internal Medicine, Faculdade de Medicina, and Telehealth Center and Cardiology Service, Hospital das Clínicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.ORCID http://orcid.org/0000-0002-2740-0042
Daniel LindholmDepartment of Medical Sciences, Clinical Epidemiology Unit, Uppsala University, Uppsala, Sweden.ORCID http://orcid.org/0000-0003-3526-0614
Thomas B SchönDepartment of Information Technology, Division of Systems and Control, Uppsala University, Uppsala, Sweden.
Johan SundströmDepartment of Medical Sciences, Clinical Epidemiology Unit, Uppsala University, Uppsala, Sweden. johan.sundstrom@uu.se.ORCID http://orcid.org/0000-0003-2247-8454

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rapid identification and localization of an acute coronary occlusion are vital to prevent myocardial damage, yet reliance on ST-segment ECG criteria misses many acute occlusion myocardial infarctions (OMI) and triggers unnecessary acute angiographies. Here, we present a trained and validated deep learning model using 540,372 emergency ECGs paired with definitive catheterization outcomes. The model has a C-statistic of ≥0.95 for OMI and ≥0.87 for non-OMI infarctions and can localize culprit lesions in the three main coronary branches, which can guide the angiographer. Performance is similar across age, sex, and ECG hardware subgroups. Obviating dependence on ST-elevations and troponins, this model for the identification and localization of OMI has the potential to shorten the time to reperfusion of an acute coronary occlusion and save resources. Because human oversight of OMI detection on the ECG is limited, randomized clinical trials with patient-relevant outcomes are warranted.

Indexed as

Coronary OcclusionDeep LearningElectrocardiographyMyocardial InfarctionAgedCoronary AngiographyFemaleHumansMaleMiddle Aged

Identifiers

PMID42129209
PMCPMC13171952

What Socratic holds

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LicenceCC BY
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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 Socratic graph.