An artificial intelligence model predicted previous or future ventricular fibrillation episodes from Brugada ECGs with a negative predictive value of 0.94±0.11 and a positive predictive value of 0.44±0.29.
Observational
Does an artificial intelligence model applied to 12-lead ECGs predict previous or future ventricular fibrillation episodes in patients with suspected Brugada syndrome?
An AI-enabled algorithm using a convolutional neural network can predict the presence of ventricular fibrillation from a Brugada ECG with high negative predictive value, potentially aiding in risk stratification for sudden cardiac death.
BACKGROUND: Brugada syndrome is a potential cause of sudden cardiac death (SCD) and is characterized by a distinct ECG, but not all patients with A Brugada ECG develop SCD. In this study we sought to examine if an artificial intelligence (AI) model can predict a previous or future ventricular fibrillation (VF) episode from a Brugada ECG. METHODS AND RESULTS: score of 0.81±0.11. The negative predictive value was 0.94±0.11 while its positive predictive value was 0.44±0.29. CONCLUSIONS: This proof-of-concept study showed that an AI-enabled algorithm can predict the presence of VF with a substantial performance. It implies that the AI model may detect a subtle ECG change that is undetectable by humans.
Nakamura et al. (Fri,) conducted a observational in Brugada syndrome. Artificial intelligence (AI) model was evaluated on Prediction of a previous or future ventricular fibrillation (VF) episode. An artificial intelligence model predicted previous or future ventricular fibrillation episodes from Brugada ECGs with a negative predictive value of 0.94±0.11 and a positive predictive value of 0.44±0.29.