Semi-automatic Approach to Estimate the Degree of Non-alcoholic Fatty Liver Disease (NAFLD) from Ultrasound Images

The early diagnosis of Non-Alcoholic Fatty Liver Disease (NAFLD) is crucial to prevent fibrosis progression or the onset of advanced chronic liver disease. Among the non-invasive methods, ultrasound (US) B-mode imaging is recommended for population screening and follow-up. Hamaguchi’s score was proposed to improve the evaluation of the fatty liver from US images. In our study, we aimed to assess objectively the Hamaguchi score through an advanced semi-automatic analysis of US images. The study encompassed a dataset of 325 bariatric patients with NAFLD diagnosed by liver biopsy who underwent ultrasound assessment at the Liver Clinic at Trieste University Hospital. The classification models for the estimation of the three Hamaguchi sub-scores were produced by semiautomatic US image analysis based on clustering and Convolutional Neural Network (CNN) with transfer learning techniques. The results showed that the produced models were able to estimate the three sub-scores with high classification accuracy. The predictive models produced for the estimation of liver brightness hepatorenal echo contrast, the diaphragm deep attenuation, and the vessel blurring sub-scores presented a classification accuracy of 92.6%, 84.8%, and 90.9%, respectively. In conclusion, in this preliminary study, the results assessed the possibility to produce the NAFLD computer-aided diagnostic models based on analysis of US images.