[Computer-aided assessment of liver fibrosis progression in patients with chronic hepatitis B: an exploratory...

Objective: To establish automatic liver fibrosis classification models by using traditional machine learning and deep learning methods and preliminaryly evaluate the efficiency. Methods: Gray scale ultrasound images and corresponding elastic images of 354 patients, 247 males and 107 females, mean age (54±12) years undergoing partial hepatectomy in Zhongshan Hospital of Fudan University from November 2014 to January 2016 were enrolled in this study. By using traditional machine learning and deep learning methods, an automatic classification model of liver fibrosis stages (S0 to S4) were established through feature extraction and classification of ultrasound image data sets and the accuracy in different classification categories of each model were calculated, by using liver biopsy as the reference standard. Results: Pathological examination showed 73 cases in pathological stage S0, 40 cases in S1, 49 cases in S2, 41 cases in S3, and 151 cases in S4. The traditional machine classification model based on support vector machine (SVM) classifier and sparse representation classifier and the deep learning classification model based on LeNet-5 neural network, their accuracy rates in the two categories (S0/S1/S2 and S3/S4) were 89.8%, 91.8% and 90.7% respectively; the accuracy rates in the three categories (S0/S1 and S2/S3 and S4) were 75.3%, 79.4% and 82.8% respectively; the accuracy in the three categories (S0 and S1/S2/S3 and S4) were 79.3%, 82.7% and 87.2% respectively. Conclusions: Computer-aided assessment of liver fibrosis progression in patients with chronic hepatitis B has a high accuracy, and can achieve a more detailed classification. This method is expected to be applied in the non-invasive evaluation of liver fibrosis in patients with hepatitis B in clinical work in the future.