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Computed Tomography Image Texture under Feature Extraction Algorithm in the Diagnosis of Effect of Specific Nursing
Yuyan Bi1, Cuifeng Jiang2, Hua Qi1
1Department of Pediatric Ward, Jinan City People's Hospital, Jinan 271199, Shandong Province, China.
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.
Insights
Specific nursing interventions significantly improved recovery outcomes in children with mycoplasma pneumonia (MP). This approach, utilizing computed tomography (CT) and gray level co-occurrence matrix (GLCM) analysis, led to faster symptom resolution and higher patient satisfaction compared to routine care.
Area of Science:
- Pediatrics
- Pulmonology
- Medical Imaging
Background:
- Mycoplasma pneumonia (MP) is a common respiratory infection in children.
- Assessing the effectiveness of nursing interventions is crucial for improving patient outcomes.
- Computed tomography (CT) imaging combined with texture analysis offers potential for objective assessment of lung changes.
Purpose of the Study:
- To evaluate the impact of a specific nursing intervention on the recovery of children diagnosed with mycoplasma pneumonia (MP).
- To compare the efficacy of specific nursing interventions against routine nursing care in pediatric MP patients.
- To assess the utility of an optimized gray level co-occurrence matrix (GLCM) algorithm for analyzing CT image texture features in MP.
Main Methods:
- A cohort of 98 children with MP was divided into two groups: specific nursing (observation group, n=49) and routine nursing (control group, n=49).
- CT images were analyzed using a feature extraction algorithm based on an optimized GLCM to evaluate lung texture before and after nursing intervention.
- Key clinical indicators including symptom resolution times, radiographic findings, and patient satisfaction were recorded and compared between groups.
Main Results:
- The specific nursing group showed a significantly lower proportion of lung texture abnormalities (increased texture, rope shadow, ground glass shadow, atelectasis, pleural effusion) post-intervention compared to the control group (P < 0.05).
- Symptom resolution, including cough disappearance, normalization of temperature, rales disappearance, and lung shadow absorption, was significantly faster in the specific nursing group (P < 0.05).
- Patient satisfaction rates were significantly higher in the specific nursing group, with lower dissatisfaction rates compared to the control group (P < 0.05).
Conclusions:
- Specific nursing interventions are more effective than routine nursing in improving the recovery progress and overall outcomes for children with MP.
- The optimized GLCM-based CT image analysis demonstrates significant value in the diagnosis and treatment monitoring of pediatric MP.
- This study highlights the importance of tailored nursing care strategies in conjunction with advanced imaging techniques for managing pediatric respiratory infections.
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