Clinical applicability of artificial intelligence electrocardiogram interpretation in Brugada syndrome: a diagnostic accuracy meta-analysis
Year: 2026
Type: article
Study designs: Systematic Review, Meta-Analysis
Abstract: INTRODUCTION: Artificial intelligence electrocardiogram (AI-ECG) interpretation has emerged as a promising approach to identify Brugada syndrome (BrS). This review sought to investigate diagnostic viability and clinical applicability of AI-ECG interpretation models for detecting BrS. METHODS: A systematic search (PubMed, Scopus, and ScienceDirect) was conducted in November 2025. STATA/BE (v17.0) was used to pool the overall metrics of the quantitative diagnostic test accuracy meta-analysis. RESULTS: Seven studies comprising 15 AI-model analyses were included. AI-ECG interpretation yielded a pooled sensitivity of 0.82 [0.77-0.87], specificity of 0.81 [0.73-0.87], negative likelihood ratio of 0.22 [0.16-0.29], positive likelihood ratio of 4.31 [2.94-6.33], and area under the curve of 0.88 [0.85-0.91]. Clinical applicability assessed using Fagan's nomogram showed minimal diagnostic value for general population screening (pretest probability 0.05%). However, in case-finding (5%) and high-risk scenarios (20%), positive results substantially increased posttest probability (to 18% and 52%, respectively), while negative results reduced it to 1% and 5%. Overall, the Fagan plot indicated a beneficial trend toward rule-in utility rather than rule-out utility. CONCLUSION: AI-ECG interpretation demonstrated a good overall diagnostic performance for detecting BrS and may serve as valuable decision-support assisting clinicians in clinically suspected or high-risk populations. PROTOCOL REGISTRATION: www.crd.york.ac.uk/prospero identifier is CRD420261298566.
Source: Future Cardiology
Authors: Kevin Nathaniel Cuandra, Christopher Daniel Tristan, Indah Kurnia Hutabarat, Vina Sari Nugrahaning Widi, Dwi Nurcahya Ananda Suwarya, Raymond Elbert Budianto, Delinda Ivania Zhavira Wibowo, Siti Faiza Aliya, Ayu Mas Eka Pradnyani, Dhyani Paramita Wahyudi, Muhammad Atha Muafa, Irnizarifka Irnizarifka
Language: English
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7204257659