ArticleJournal of healthcare engineering2021
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.
Article in Journal of healthcare engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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) (
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