Key result
Deep learning ECG model detects type 1 Brugada syndrome with ~88% sensitivity, outperforming cardiologists.
Why the study?
Automatic screening for the ECG pattern of Brugada syndrome by a deep learning model may allow early identification of patients to receive life-saving therapy.
Does a deep learning-enabled ECG model improve the diagnostic sensitivity for type 1 Brugada ECG pattern compared to board-certified cardiologists?
Observational (n=552)
Yes
Does a deep learning-enabled ECG model improve the diagnostic sensitivity for type 1 Brugada ECG pattern compared to board-certified cardiologists?
A deep learning ECG model demonstrated robust diagnostic performance for identifying the rare type 1 Brugada ECG pattern, offering significantly higher sensitivity than practicing cardiologists.
Answers are grounded in this paper and cite their sources.
May improve ECG detection of type 1 Brugada pattern; leaves open prospective validation before clinical use.
BACKGROUND 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. OBJECTIVE To develop a deep learning-enabled ECG model for diagnosing Brugada syndrome. METHODS 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. RESULTS 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%). CONCLUSIONS 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. CLINICALTRIAL
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Liu et al. (2020) conducted an observational in Brugada syndrome (n=552). Deep learning-enabled ECG model vs. Board-certified practicing cardiologists was evaluated on Diagnosis of type 1 Brugada ECG pattern (sensitivity) (AUC 0.96). A deep learning-enabled ECG model diagnosed type 1 Brugada syndrome with an AUC of 0.96 and higher sensitivity (88.4%) than board-certified cardiologists (62.7%).
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