Evidence map›Paper›PMID 40675022›Full record

ArticleJACC. Advances2025

Fully Automated Diagnosis of Acute Myocardial Infarction Using Electrocardiograms and Multimodal Deep Learning.

Lukas Hilgendorf, Petur Petursson, Erik Andersson, Aidin Rawshani, Deepak L Bhatt, Truls Råmunddal, Vibha Gupta, Kristofer Skoglund, Elmir Omerovic, Helen Sjöland and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in JACC. Advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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

14 authors.

Lukas HilgendorfInstitute of Medicine, Department of Molecular and Clinical Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Wallenberg Centre for Molecular and Translational Research (WCMTM), Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden. Electronic address: lukas.hilgendorf@gu.se.
Petur PeturssonInstitute of Medicine, Department of Molecular and Clinical Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Department of Cardiology, Sahlgrenska University Hospital, Gothenburg, Sweden.
Erik AnderssonInstitute of Medicine, Department of Molecular and Clinical Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Wallenberg Centre for Molecular and Translational Research (WCMTM), Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Aidin RawshaniInstitute of Medicine, Department of Molecular and Clinical Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Deepak L BhattMount Sinai Fuster Heart Hospital, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Truls RåmunddalInstitute of Medicine, Department of Molecular and Clinical Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Department of Cardiology, Sahlgrenska University Hospital, Gothenburg, Sweden.
Vibha GuptaInstitute of Medicine, Department of Molecular and Clinical Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Wallenberg Centre for Molecular and Translational Research (WCMTM), Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Kristofer SkoglundInstitute of Medicine, Department of Molecular and Clinical Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Department of Cardiology, Sahlgrenska University Hospital, Gothenburg, Sweden.
Elmir OmerovicInstitute of Medicine, Department of Molecular and Clinical Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Department of Cardiology, Sahlgrenska University Hospital, Gothenburg, Sweden.
Helen SjölandInstitute of Medicine, Department of Molecular and Clinical Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Amar TahaInstitute of Medicine, Department of Molecular and Clinical Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Department of Cardiology, Sahlgrenska University Hospital, Gothenburg, Sweden.
David KimDepartment of Emergency Medicine, Stanford University, Stanford, California, USA.
Peter LundgrenInstitute of Medicine, Department of Molecular and Clinical Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Department of Cardiology, Sahlgrenska University Hospital, Gothenburg, Sweden; Centre for Digital Health, Sahlgrenska University Hospital, Gothenburg, Sweden.
Araz RawshaniInstitute of Medicine, Department of Molecular and Clinical Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Wallenberg Centre for Molecular and Translational Research (WCMTM), Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Department of Cardiology, Sahlgrenska University Hospital, Gothenburg, Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRapid detection of acute myocardial infarction (AMI) reduces morbidity and mortality. Deep learning may enhance automated electrocardiogram (ECG) interpretation.

objectivesThe purpose of the study was to develop and validate a deep learning model for AMI detection using ECG data, demographics, and symptoms.

methodsThis retrospective cohort study used ECG data from 2 centers in Västra Götaland County, Sweden (January 2015-June 2023), for model training and validation, with a third center for external testing. Patients with chest pain or dyspnea who received a prehospital or in-hospital ECG were included. A residual convolutional neural network was trained on ECG features, age, sex, and symptoms to predict AMI, defined by International Classification of Diseases codes at discharge. Performance was assessed using area under the receiver operating characteristic, sensitivity, and specificity.

resultsThe study included 104,507 individuals (208,366 ECGs), with 8.17% in the training set and 8.59% in the external set diagnosed with AMI. The model achieved AUROCs of 0.8221 ± 0.0101 (internal validation ± 95% CI) and 0.8314 ± 0.0085 (external validation). Performance was consistent across sex but slightly lower for ambulance-arriving patients (area under the receiver operating characteristic: 0.8081 ± 0.0095). Saliency maps highlighted focus on ST segments and T waves.

conclusionsThe deep learning model demonstrated strong AMI detection across diverse patient groups. A randomized trial is needed to compare its performance with emergency physicians.

Identifiers

PMID40675022
PMCPMC12284675

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.