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Background: Inferior biphasic or inverted T waves have been described on a standard 12-lead ECG in some, but not all patients with malignant mitral valve prolapse (MVP). In contrast, complex ventricular ectopy (ComVE) is associated with myocardial fibrosis and increased mortality in MVP and may represent a better marker of arrhythmic risk. We hypothesize that an ECG-based machine-learning model can identify MVP with ComVE and/or myocardial fibrosis on cardiac magnetic resonance (CMR) imaging within a large ECG database beyond traditional ECG criteria. Methods: A deep convolutional neural network (DNN) was trained to detect ComVE using 12 leads in 6,916 ECGs from 569 MVP patients evaluated at the University of California San Francisco (UCSF) between 2012 and 2020. A DNN was also trained using 87 UCSF MVP patients with available contrast CMR to detect late gadolinium enhancement (LGE). Results: The prevalence of ComVE was 160/569 or 28% (20 or 3% with cardiac arrest or sudden arrhythmic death). The area under the curve (AUC) of the DNN to detect ComVE was 0.81 (95% CI, 0.78-0.84) (Figure). AUC remained high even after excluding patients with moderate-severe mitral regurgitation (MR) [0.80 (95% CI, 0.77-0.83)], or with bileaflet MVP [0.81 (95% CI, 0.76-0.85)]. The top ECG segments able to discriminate ComVE vs no ComVE were related to ventricular depolarization and repolarization (early-mid ST and QRS fromV1, V3, and III). LGE in the papillary muscles or basal inferolateral wall was present in 22 of 87 (25%) with available CMR. The AUC for detection of LGE was 0.75 (95% CI, 0.68-0.82). Conclusions: A deep-learning model can detect MVP at risk for ventricular arrhythmias and fibrosis from standard 12-lead ECGs, and can identify novel ECG correlates of arrhythmic risk regardless of leaflet involvement or mitral regurgitation severity. ECG-based deep-learning may help select, within a large echocardiographic database, those MVP patients requiring closer follow-up and/or a CMR.