Data-Driven Classification of Fatty Liver From 3D Unenhanced Abdominal CT Scans

Fatty liver disease is a prevalent condition with significant health implications and early detection may prevent adverse outcomes. In this study, we developed a data-driven classification framework using deep learning to classify fatty liver disease from unenhanced abdominal CT scans. The framework consisted of a two-stage pipeline: 3D liver segmentation and feature extraction, followed by a deep learning classifier. We compared the performance of different deep learning feature representations with volumetric liver attenuation, a hand-crafted radiomic feature. Additionally, we assessed the predictive capability of our classifier for the future occurrence of fatty liver disease. The deep learning models outperformed the liver attenuation model for baseline fatty liver classification, with an AUC of 0.90 versus 0.86, respectively. Furthermore, our classifier was better able to detect mild degrees of steatosis and demonstrated the ability to predict future occurrence of fatty liver disease.