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.
Highlights
Sepsis refers to a systemic inflammatory syndrome caused by the imbalance of human response to infection, which can be life threatening and lead to systemic organ dysfunction [1]
The CT images of patients with severe sepsis caused by pulmonary infection were divided by convolution neural network algorithm
Fasting plasma glucose (FPG) was 7:31 ± 1:64 and fasting insulin (FIN) was 16:12 ± 1:92 in the control group, and FPG was 6:37 ± 1:52 and FIN was 13:84 ± 1:86 in the experimental group, which were higher in the experimental group than those in the control group, and the difference between the two groups was statistically significant (P < 0:05)
Summary
Sepsis refers to a systemic inflammatory syndrome caused by the imbalance of human response to infection, which can be life threatening and lead to systemic organ dysfunction [1]. Convolution neural network algorithm is a new intelligent algorithm in recent years It can divide medical image images efficiently and quickly, with an average time of 0.9 s [8]. Convolution neural network algorithm is an automatic intelligent algorithm based on computer technology and mathematical data processing. It can extract the features of the region of interest of medical image without human intervention, classify the features, and output the target data [9]. In this study, the convolutional neural network algorithm was used to segment the CT images of patients with pulmonary infection to severe sepsis in order to assist clinicians in the early diagnosis of severe sepsis
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