Abstract
Brugada Syndrome (BrS) is a hereditary cardiac condition associated with an elevated risk of lethal arrhythmias, making precise and prompt diagnosis vital to prevent life-threatening outcomes. The diagnosis of BrS is challenging due to the requirement of invasive drug challenge tests, limited human visual capacity to detect subtle electrocardiogram (ECG) patterns, and the transient nature of the disease. Artificial intelligence (AI) can detect almost all patterns of BrS in ECG, some of which are even beyond the capability of expert eyes. AI is subcategorized into several models, with deep learning being considered the most beneficial, boasting its highest accuracy among the other models. With the capability to discriminate subtle data and analyze extensive datasets, AI has achieved higher accuracy, sensitivity, and specificity compared to trained cardiologists. Meanwhile, AI proficiency in managing complex data enables us to discover unclassified genetic variants. AI can also analyze data extracted from induced pluripotent stem cell-derived cardiomyocytes to distinguish BrS from other inherited cardiac arrhythmias. The aim of this study is to present a synopsis of the evolution of various algorithms of artificial intelligence utilized in the diagnosis of BrS and compare their diagnostic abilities to trained cardiologists. In addition, the application of AI for classification of BrS gene variants is also briefly discussed.