Evidence map›Paper›PMID 40461897›Full record

SynthesisJournal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing2026

A systematic review and meta-analysis of artificial intelligence ECGs performance in the diagnosis of Brugada Syndrome.

Lucas M Barbosa, Roberto Mazetto, Maria L R Defante, Vânio L J Antunes, Vinicius Martins Rodrigues Oliveira, Douglas Cavalcante, Luanna Paula Garcez de Carvalho Feitoza, Ivo Queiroz, André Luiz Carvalho Ferreira, Guilherme Almeida and 5 more

Abstract readSystematic ReviewMeta-Analysis
PubMed Publisher
In one paragraph

Synthesis in Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing, 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. 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

15 authors.

Lucas M BarbosaMedicine Department, Federal University of Minas Gerais, Rua Alfredo Balena, Belo Horizonte, 190, Brazil. lucasmend78@gmail.com.
Roberto MazettoMedicine Department, Amazonas State University, Manaus, Brazil.
Maria L R DefanteMedicine Department, Redentor University Center, Itaperuna, Brazil.
Vânio L J AntunesMedicine Department, Federal University of Health Sciences of Porto Alegre, Porto Alegre, Brazil.
Vinicius Martins Rodrigues OliveiraMedicine Department, Federal University of Goiás, Goiás, Brazil.
Douglas CavalcanteHospital Israelita Albert Einstein, São Paulo, Brazil.
Luanna Paula Garcez de Carvalho FeitozaCenter University Fametro, Medicine Department, Manaus, Brazil.
Ivo QueirozRadiology Department, University of Wisconsin Madison, Wisconsin, USA.
André Luiz Carvalho FerreiraMedicine Department, Pontifical Catholic University of Paraná, Curitiba, Brazil.
Guilherme AlmeidaMedicine Department, Federal University of Sergipe, Aracaju, Brazil.
Elísio BulhõesFaculty of Higher Superior of the Amazon Reunida, Medicine Department, Pará, Brazil.
Maria do Carmo P NunesMedicine Department, Federal University of Minas Gerais, Rua Alfredo Balena, Belo Horizonte, 190, Brazil.
Mauricio Ibrahim ScanavaccaInstituto Do Coração (InCor), University of São Paulo, Arrhythmia Unit, São Paulo, Brazil.
Francisco DarrieuxInstituto Do Coração (InCor), University of São Paulo, Arrhythmia Unit, São Paulo, Brazil.
Josep BrugadaCardiology Department, Hospital Clinic Cardiovascular Institute, University of Barcelona, Barcelona, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBrugada syndrome (BrS) is a serious condition linked to sudden cardiac death in individuals who are otherwise healthy. Notably, drug-induced BrS accounts for 50% to 70% of all documented cases. The utilization of artificial intelligence (AI) models in the analysis of electrocardiograms (ECGs) represents a promising approach for the detection of BrS. PURPOSE: This meta-analysis aims to evaluate the effectiveness of AI models in diagnosing BrS through ECG analysis.

methodsWe conducted a systematic search across PubMed, Embase, and Cochrane databases, focusing on AI-based models for ECG analysis related to BrS detection. Key outcomes measured included sensitivity, specificity, and the summary receiver operating characteristic (SROC) curve. Pooled proportions were calculated using a random-effects model with 95% confidence intervals (CIs), and heterogeneity was using Zhou and Dendukuri I

resultsOur analysis included six studies encompassing ECG data from 2,179 patients, all employing AI algorithms for ECG interpretation. The quantitative analysis revealed an area under the curve (AUC) of 0.898, a sensitivity of 78.9% (95% CI: 69.6 to 85.9), and a specificity of 87.7% (95% CI: 79.9 to 92.7). Notably, the sensitivity analysis without Zanchi et al., significantly reduced the heterogeneity (I

conclusionAI-driven ECG interpretation demonstrates to be a viable option in detecting BrS.

Indexed as

Artificial IntelligenceBrugada SyndromeElectrocardiographyAlgorithmsFemaleHumansMaleSensitivity and SpecificityArtificial IntelligenceBrugada SyndromeDiagnostic Meta-analysisDiagnostic performance

Identifiers

PMID40461897

What OpenQuestion holds

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Registered trials

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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.