August 1, 2023Open Access

Identifying Mitral Valve Prolapse at Risk for Arrhythmias and Fibrosis From Electrocardiograms Using Deep Learning

PICO estructurado

Does deep learning analysis of 12-lead ECGs identify risk for ventricular arrhythmias, death, and/or fibrosis in patients with mitral valve prolapse?

Población

Patients with Mitral Valve Prolapse (MVP)

Intervención

Deep learning (CNN) analysis of 12-lead ECGs

Resultado

Risk for ventricular arrhythmias, death and/or fibrosissurrogate

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.

Resumen

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.

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