CT Image Feature under Intelligent Algorithm in the Evaluation of Continuous Blood Purification in the Treatment and Nursing of Pulmonary Infection-Caused Severe Sepsis. (28th November 2021)

• Record Type: Journal Article
• Authors: Liu, Liping; Liu, Yanyan; Xing, Aimin; Chen, Siyu; Gu, Mingli
• Other Names: Khalaf Osamah Ibrahim Academic Editor.
• Is Part Of: Computational and mathematical methods in medicine. Volume 2021(2021)
• Publication Date: 2021-11-28
• Subjects: Medicine -- Computer simulation -- Periodicals; Medicine -- Mathematical models -- Periodicals; 610.11
• DOI: 10.1155/2021/2281327
• Languages: English
• ISSNs: 1748-670X

Abstract

Abstract : This study was to explore the CT image features based on intelligent algorithm to evaluate continuous blood purification in the treatment of severe sepsis caused by pulmonary infection and nursing. 50 patients in the hospital were selected as the research objects. Convolutional neural network algorithm was used to segment CT images of severe sepsis caused by pulmonary infection. They were randomly divided into 25 cases of experimental group and 25 cases of control group. The experimental group was given continuous blood purification treatment, combined with comprehensive nursing. The control group was given routine treatment and basic nursing. Fasting plasma glucose (FPG) and fasting insulin (FIN), interleukin-6 (IL-6), tumor necrosis factor (TNF- α ), high-sensitivity c-reactive protein (hs-CRP) levels, CD3 +, CD4 +, CD4 +/ CD8 + levels, ICU monitoring time, malnutrition inflammation score (MIS), and incidence of adverse events were compared between the two groups before and after treatment. There was no difference in FPG and FIN between the two groups before treatment. After treatment, the FPG and FIN of the experimental group were lower than those of the control group, and there was statistical significance (P < 0.05 ). There was no difference in IL-6, TNF- α, and hs-CRP between the two groups before treatment. After treatment, IL-6, TNF- α, and hs-CRP in the experimental group were lower than those in the control group. There was no difference in the percentage of CD3 +, CD4 +, and CD4 +/ CD8 + between the two groups before treatment. After treatment, the CD3 +, CD4 +, and CD4 + /CD8 + in the experimental group were higher than those in the control group. The ICU monitoring time, MIS, and incidence of adverse events in the experimental group were lower than those in the control group (P > 0.05 ). Convolutional neural network algorithm can accurately identify and segment CT images of patients with severe sepsis, which has high clinical application value. Continuous blood purification therapy can effectively control blood glucose level, improve immune function, and reduce the content of inflammatory factors in patients with severe sepsis caused by pulmonary infection. Effective nursing measures can improve the therapeutic effect.