Discrimination of Chronic Liver Disease in Non-contrast CT Images using CNN-Deep Learning

Abstract

Recently, there have been considerable efforts to develop non-invasive image-based method for diagnosis, staging, and monitoring of chronic liver disease (CLD). This study developed a deep learning (DL) algorithm for discriminating the CLD using non-contrast abdominal CT images. This study enrolled 499 patients with CLD and 122 healthy controls. The main structure of DL algorithm used GoogLeNet-V3 and several modules fine-tuned for this study. In the test data sets, the DL algorithm had a discrimination accuracy of 99.4% (loss is about 0.7%) and an AUROC of 0.998, for diagnosis between normal controls and CLD patients. In the validation test, we achieve good validation result as follows: specificity (for healthy controls) 0.98292 (error rate: 0.01708) and sensitivity (for CLD) 0.99469 (error rate: 0.00531), respectively. Our deep learning algorithm would be useful for accurate discrimination in CLD from the abdominal CT images without the use of contrast agent. Further study is needed to diagnose the disease severity within CLD patient group for clinical application.