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• W4252772074 abstract "<sec> <title>BACKGROUND</title> Brugada syndrome is a rare inherited arrhythmia with a unique electrocardiogram (ECG) pattern (type 1 Brugada ECG pattern), which is a major cause of sudden cardiac death in young people. Automatic screening for the ECG pattern of Brugada syndrome by a deep learning model gives us the chance to identify these patients at an early time, thus allowing them to receive life-saving therapy. </sec> <sec> <title>OBJECTIVE</title> To develop a deep learning-enabled ECG model for diagnosing Brugada syndrome. </sec> <sec> <title>METHODS</title> A total of 276 ECGs with a type 1 Brugada ECG pattern (276 type 1 Brugada ECGs and another randomly retrieved 276 non-Brugada type ECGs for one to one allocation) were extracted from the hospital-based ECG database for a two-stage analysis with a deep learning model. We first trained the network to identify right bundle branch block (RBBB) pattern, and then, we transferred the first-stage learning to the second task to diagnose the type 1 Brugada ECG pattern. The diagnostic performance of the deep learning model was compared to that of board-certified practicing cardiologists. The model was also validated by the independent international data of ECGs. </sec> <sec> <title>RESULTS</title> The AUC (area under the curve) of the deep learning model in diagnosing the type 1 Brugada ECG pattern was 0.96 (sensitivity: 88.4%, specificity: 89.1%). The sensitivity and specificity of the cardiologists for the diagnosis of the type 1 Brugada ECG pattern were 62.7±17.8%, and 98.5±3.0%, respectively. The diagnoses by the deep learning model were highly consistent with the standard diagnoses (Kappa coefficient: 0.78, McNemar test, P = .86). However, the diagnoses by the cardiologists were significantly different from the standard diagnoses, with only moderate consistency (Kappa coefficient: 0.60, McNemar test, P = 2.35x10-22). For the international validation, the AUC of the deep learning model for diagnosing the type 1 Brugada ECG pattern was 0.99 (sensitivity: 85.7%, specificity: 100.0%). </sec> <sec> <title>CONCLUSIONS</title> We presented the first deep learning-enabled ECG model for diagnosing Brugada syndrome, which is a robust screening tool with better diagnostic sensitivity than that of cardiologists. </sec> <sec> <title>CLINICALTRIAL</title> <p /> </sec>" @default.
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• W4252772074 date "2020-07-06" @default.
• W4252772074 modified "2023-09-23" @default.
• W4252772074 title "A Deep Learning-enabled Electrocardiogram Model for the Identification of a Rare Inherited Arrhythmia: Brugada Syndrome (Preprint)" @default.
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• W4252772074 doi "https://doi.org/10.2196/preprints.22163" @default.
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