Cardiovascular disease and all-cause mortality risk prediction from abdominal CT using deep learning

Cardiovascular disease is the number one cause of mortality worldwide. Risk prediction can help incentivize lifestyle changes and inform targeted preventative treatment. In this work we explore utilizing a convolutional neural network (CNN) to predict cardiovascular disease risk from abdominal CT scans taken for routine CT colonography in otherwise healthy patients aged 50-65. We find that adding a variational autoencoder (VAE) to the CNN classifier improves its accuracy for five year survival prediction (AUC 0.787 vs. 0.768). In four-fold cross validation we obtain an average AUC of 0.787 for predicting five year survival and an AUC of 0.767 for predicting cardiovascular disease. For five year survival prediction our model is significantly better than the Framingham Risk Score (AUC 0.688) and of nearly equivalent performance to method demonstrated in Pickhardt et al. (AUC 0.789) which utilized a combination of five CT derived biomarkers.