Studies have been conducted to predict outcomes of extracorporeal shock wave lithotripsy (ESWL) using clinical information from patients. However, non-stone areas in CT images were not included, as the main predictors were stones and patient body mass index. In this paper, two types of convolutional neural networks are applied to non-stone areas of CT images to predict ESWL outcomes. Resulting AUC of 0.94 and accuracy of 0.91 were achieved. Multivariate analysis was also performed and confirmed that the proposed predictors are independent of the conventional stone factors.
