Does deep learning analysis of 12-lead ECGs identify risk for ventricular arrhythmias, death, and/or fibrosis in patients with mitral valve prolapse?
Deep learning analysis of 12-lead ECGs can identify patients with mitral valve prolapse who are at high risk for arrhythmias, death, or fibrosis, potentially guiding the need for closer follow-up or CMR.
CNN-analyzed 12-lead ECGs can detect MVP at risk for ventricular arrhythmias, death and/or fibrosis and can identify novel ECG correlates of arrhythmic risk. ECG-based CNNs may help select those MVP patients requiring closer follow-up and/or a CMR.
Tison et al. (Tue,) studied this question.