Computed Tomography Image Features under Convolutional Neural Network Algorithm in Analysis of Inflammatory Factor Level and Prognosis of Patients with Hepatitis B Virus-Associated Acute-on-Chronic Liver Failure

Abstract

This study aimed to explore the application value of three-dimensional (3D) convolutional neural networks (3D-CNN)-based computed tomography (CT) image intelligent segmentation model in the identification of lesions of patients with hepatitis B virus-associated acute-on-chronic liver failure (HBV-ACLF). A total of 30 patients with HBV-ACLF, 30 patients with chronic HBV hospitalized in hospital, and 30 healthy volunteers were selected as subjects. Liver function and serum inflammatory factors were measured in each group, and the 3D-CNN algorithm model was applied to CT imaging. The results showed that the levels of interleukin (IL)-6, IL-26, and IL-37 in the HBV-ACLF group were the highest, which were 128.43 ± 45.16 pg/mL, 1237.47 ± 536.22 pg/mL, and 50.83 ± 7.62 pg/mL, respectively. Total bilirubin (TBIL) ( $P=0.035$ ) and IL-26 ( $P=0.013$ ) were independent predictors that affected the prognosis of HBV-ACLF patients. The results of lesion segmentation showed that the Dice coefficient of 3D-CNN low-density focus and enhanced focus segmentation was the highest (0.821 ± 0.07 and 0.773 ± 0.071), and the marked area was close to the area manually drawn by the doctor. 3D CNN was superior to other algorithms in the number of nodular lesions detected (533), sensitivity (97.5%), and missed detection rate (0.52%) ( $P<0.05$ ). In short, IL-26 may become a useful biomarker in the treatment of HBV-ACLF. The 3D-CNN model improved the segmentation performance of lesions in CT images of HBV-ACLF patients, which provided a reference for the diagnosis and prognosis of HBV-ACLF.