Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing · 2025
A systematic review and meta-analysis of artificial intelligence ECGs performance in the diagnosis of Brugada Syndrome.
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Barbosa Lucas M, Mazetto Roberto, Defante Maria L R, Antunes Vânio L J, Oliveira Vinicius Martins Rodrigues, Cavalcante Douglas, Feitoza Luanna Paula Garcez de Carvalho, Queiroz Ivo, Ferreira André Luiz Carvalho, Almeida Guilherme, Bulhões Elísio, Nunes Maria do Carmo P, Scanavacca Mauricio Ibrahim, Darrieux Francisco, Brugada Josep
Аннотация
BACKGROUND: Brugada 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.
METHODS: We 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 I2 approach. Additionally, a leave-one-out sensitivity analysis was performed to evaluate the impact of each one of the included studies on the pooled results and heterogeneity. All statistical analyses were conducted using R version 4.4.2.
RESULTS: Our 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 (I2 = 0%). However, the other analyses corroborated with our general findings.
CONCLUSION: AI-driven ECG interpretation demonstrates to be a viable option in detecting BrS.
PURPOSE: This meta-analysis aims to evaluate the effectiveness of AI models in diagnosing BrS through ECG analysis.
METHODS: We 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 I2 approach. Additionally, a leave-one-out sensitivity analysis was performed to evaluate the impact of each one of the included studies on the pooled results and heterogeneity. All statistical analyses were conducted using R version 4.4.2.
RESULTS: Our 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 (I2 = 0%). However, the other analyses corroborated with our general findings.
CONCLUSION: AI-driven ECG interpretation demonstrates to be a viable option in detecting BrS.