In patients with stable Coronary Artery Disease (CAD), the identification of lesions which will be responsible of a myocardial infarction (MI) during follow-up remains a daily challenge. In this work, we propose to predict culprit stenosis by applying a deep learning framework on image stenosis obtained from patient data. Preliminary results on a data set of 746 lesions obtained from angiographies confirm that deep learning can indeed achieve significant predictive performance, and even outperforms the one achieved by a group of interventional cardiologists. To the best of our knowledge, this is the first work that leverages the power of deep learning to predict MI from coronary angiograms, and it opens new doors towards predicting MI using data-driven algorithms.