Key result
AI algorithms achieve high diagnostic accuracy for Brugada syndrome with a ~0.88 AUC.
Why the study?
To determine the diagnostic and prognostic utility of artificial intelligence and machine learning algorithms in Brugada syndrome.
Do artificial intelligence/machine learning algorithms improve the diagnostic and prognostic accuracy for Brugada Syndrome?
Systematic Review (n=3,727)
Do artificial intelligence/machine learning algorithms improve the diagnostic and prognostic accuracy for Brugada Syndrome?
Effect estimate: SAUROC 0.877
AI/ML algorithms demonstrate high diagnostic and prognostic accuracy for Brugada syndrome, highlighting their potential to streamline diagnosis and risk stratification without the need for high-risk sodium channel blocker challenges.
Aim: The objective of this systematic review and meta-analysis was to determine the diagnostic and prognostic utility of artificial intelligence/machine learning (AI/ML) algorithms in Brugada Syndrome (BrS). Methods: A systematic review and meta-analysis of the literature was conducted in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. MEDLINE, EMBASE, SCOPUS, and WEB OF SCIENCE databases were searched for relevant articles. Abstract and title screening, full-text review, and data extraction were conducted independently by two of the authors. Conflicts were resolved via discussion among authors. A risk-of-bias assessment was performed using the QUADAS-2 tool for diagnostic studies and the PROBAST tool for prognostic studies. Forest plots and the summary area under the receiver operating characteristic (SAUROC) curve were done in R. Results: A total of 12 papers were included in our study. Among the best-performing diagnostic algorithms from each study, the sensitivity and specificity ranged from 0.80 to 0.89 and 0.74 to 0.97, respectively. In overall studies, sensitivity was 0.845 ± 0.014 and specificity was 0.892 ± 0.062 using a random effects model. A pooled analysis of the summary area under the receiver operating characteristic curve (SAUROC) was 0.77 for diagnostic studies. Prognostic studies showed good performance as well, with the AUC of the best-performing prognostic algorithms ranging from 0.71 to 0.90. Conclusions: Overall, AI/ML algorithms had high diagnostic and prognostic accuracy. These results highlight the potential of AI/ML algorithms for the diagnosis and prognosis of BrS and permit a choice of the best-performing ML algorithms.
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Leong et al. (2024) conducted a systematic review in Brugada syndrome (n=3,727). Artificial intelligence and machine learning algorithms was evaluated on Summary area under the receiver operating characteristic curve (SAUROC) for diagnosis of Brugada syndrome (SAUROC 0.877). Artificial intelligence and machine learning algorithms demonstrated high diagnostic accuracy for Brugada syndrome, achieving a pooled sensitivity of 0.848, specificity of 0.892, and a summary AUC of 0.877.
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