Abstract
To evaluate the effect of specific nursing intervention in children with mycoplasma pneumonia (MP), a feature extraction algorithm based on gray level co-occurrence matrix (GLCM) was proposed and combined with computed tomography (CT) image texture features. Then, 98 children with MP were rolled into the observation group with 49 cases (specific nursing) and the control group with 49 cases (routine nursing). CT images based on feature extraction algorithm of optimized GLCM were used to examine the children before and after nursing intervention, and the recovery of the two groups of children was discussed. The results showed that the proportion of lung texture increase, rope shadow, ground glass shadow, atelectasis, and pleural effusion in the observation group (24.11%, 3.86%, 8.53%, 15.03%, and 3.74%) was significantly lower than that in the control group (28.53%, 10.23%, 13.34%, 21.15%, and 8.13%) after nursing (P < 0.05). There were no significant differences in the proportion of small patchy shadows, large patchy consolidation shadows, and bronchiectasis between the observation group and the control group (P > 0.05). In the course of nursing intervention, in the observation group, the disappearance time of cough, normal temperature, disappearance time of lung rales, and absorption time of lung shadow (2.15 ± 0.86 days, 4.81 ± 1.14 days, 3.64 ± 0.55 days, and 5.96 ± 0.62 days) were significantly shorter than those in the control group (2.87 ± 0.95 days, 3.95 ± 1.06 days, 4.51 ± 1.02 days, and 8.14 ± 1.35 days) (P < 0.05). After nursing intervention, the proportion of satisfaction and total satisfaction in the experimental group (67.08% and 28.66%) was significantly higher than that in the control group (40.21% and 47.39%), while the proportion of dissatisfaction (4.26%) was significantly lower than that in the control group (12.4%) (P < 0.05). To sum up, specific nursing intervention was more beneficial to improve the progress of characterization recovery and the overall recovery effect of children with MP relative to conventional nursing. CT image based on feature extraction algorithm of optimized GLCM was of good adoption value in the diagnosis and treatment of MP in children.
Highlights
Mycoplasma pneumonia (MP) is a common respiratory disease in children
Feature Extraction Algorithm Based on Optimized gray level co-occurrence matrix (GLCM)
The GLCM can calculate the similarity between different pixels at a specific distance and gray level in a specific direction to describe the overall texture information of the image [15]
Summary
Mycoplasma pneumonia (MP) is a common respiratory disease in children. It is caused by mycoplasma pneumoniae infection and is often associated with atelectasis and large lung infiltration. It can cause extrapulmonary multisystem complications [1, 2]. Mycoplasma pneumonia in children generally presents a subacute onset, and the clinical symptoms are usually dry cough, sore throat, headache, and unsteady fever. Specific nursing is a targeted approach to quality care, whose core is people-oriented. It embodies the humanistic spirit and respects the life value, personal dignity, and personal privacy of patients. Patients are greatly helped in rehabilitation treatment by advocating humanized service concept, paying attention to humanized nursing management, and creating humanized service environment [5, 6]
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