Anatomy-informed multimodal learning for myocardial infarction prediction

In patients with coronary artery disease the prediction of future cardiac events such as myocardial infarction (MI) remains a major challenge. In this work, we propose a novel anatomy-informed multimodal deep learning framework to predict future MI from clinical data and Invasive Coronary Angiography (ICA) images. The performance of our framework on a clinical study of 445 patients admitted with acute coronary syndromes confirms that multimodal learning increases the predictive power and achieves a relatively good performance (AUC: $0.67\pm0.04$ \& F1-Score: $0.36\pm0.12$), which outperforms the prediction obtained by each modality independently as well as that of two interventional cardiologists. To the best of our knowledge, this is the first and promising attempt towards combining multimodal data through a deep learning framework for future MI prediction.