# Evaluation of Glucocorticoid Therapy in Asthma Children with Small Airway Obstruction Based on CT Features of Deep Learning

Authors: Wei Zhang; Yang Wang
Journal: Computational and Mathematical Methods in Medicine
 (2021)

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## Abstract

This study was aimed at exploring the treatment of asthma children with small airway obstruction in CT imaging features of deep learning and glucocorticoid. A total of 145 patients meeting the requirements in hospital were included in this study, and they were randomly assigned to receive aerosolized glucocorticoid (n=45), aerosolized glucocorticoid combined with bronchodilator (n=50), or oral steroids (n=50) for 4 weeks after discharge. The lung function and fractional exhaled nitric oxide (FENO) indexes of the three groups were measured, respectively, and then the effective rates were compared to evaluate the clinical efficacy of glucocorticoids with different administration methods and combined medications in the short-term maintenance treatment after acute exacerbation of asthma. Deep learning algorithm was used for CT image segmentation. The CT image is sent to the workbench for processing on the workbench, and then the convolution operation is performed on each input pixel point during the image processing. After 4 weeks of maintenance treatment, FEF50 %, FEF75 %, and MMEF75/25 increased significantly, and FENO decreased significantly (P<0.01). The improvement results of FEF50 %, FEF75 %, MMEF75/25, and FENO after maintenance treatment were as follows: the oral hormone group was the most effective, followed by the combined atomization inhalation group, and the hormone atomization inhalation group was the least effective. The differences among them were statistically significant (P<0.05). The accuracy of artificial intelligence segmentation algorithm was 81%. All the hormones were more effective than local medication in the treatment of small airway function and airway inflammation. In the treatment of aerosol inhalation, the hormone combined with bronchiectasis drug was the most effective in improving small airway obstruction and reducing airway inflammation compared with single drug inhalation. Deep learning CT images are simple, noninvasive, and intuitively observe lung changes in asthma with small airway functional obstruction. Asthma with small airway functional obstruction has high clinical diagnosis and evaluation value.


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## Body

## 1. Introduction

Tracheal asthma is a common respiratory disease in children, and 332 million people have bronchial asthma. Nonallergic asthma can occur at all ages with viral respiratory infection [1]. In children, nonallergic asthma can be relieved in adolescence, especially in boys. In adults, nonallergic asthma is more common in women, especially in obese people [2]. Allergic asthma in children is usually associated with eczema, rhinitis or food allergy, family history of asthma, and viral respiratory infection. The possibility of asthma continuation or recurrence increases with children’s early sensitivity to allergens and exposure to tobacco. Most children can control asthma well by inhaling low and medium doses of glucocorticoids [3]. However, 4-9% of children with refractory asthma still have significant asthma after the standardized treatment of medium and high doses of glucocorticoids, which may damage their lung function and affect their study and life, which should be paid attention to by pediatricians.Glucocorticoids and adrenergic receptors are larger in the stingy pathway, and the airway is more responsive to drugs, but the pathophysiological characteristics of their secretion are difficult to be clear. The identification and evaluation of small airway disease (SAD) are of great significance for the clinical diagnosis of asthma, the evaluation of asthma control level, and the judgment of prognosis [4]. According to the research at home and abroad, small airway function is also related to patients’ health status, clinical symptoms, airway reactivity, quality of life, and acute exacerbation of asthma [5]. The occurrence of small airway inflammation and airway remodeling usually reduces the airway responsiveness to drugs and aggravates the progress of the disease. The detection of fractional exhaled nitric oxide (FENO) is a commonly used method for detecting airway inflammation in medicine [6]. The intervention and treatment of small airway obstruction can delay the progress of the disease. It has clinical significance for controlling airway inflammation, alleviating asthma symptoms, and reducing the incidence of acute exacerbation [7]. Clinically, the percentage of FEV1 in the predicted value (FEV1%) and the percentage of FEV1 in forced vital capacity (FVC) (FEV1/FVC) are commonly used to evaluate airway function. After treatment, FEV1% and FEV1/FVC of asthmatic patients were significantly improved, that is, airway airflow restriction was significantly relieved [8].Glucocorticoid is a basic drug for asthma patients, which can act on many links of airway inflammatory response. It can effectively reduce capillary permeability, prevent pulmonary edema, alleviate bronchospasm, improve lung function, and then control the occurrence of asthma.β2 receptor agonists can open bronchial smooth muscle, enhance the clearance of bronchial mucociliary epithelium, and reduce mucus secretion to relieve asthma attacks. The local use of atomized inhalation can make the drug directly to the target organs of respiratory patients. Its safety and efficiency are higher than that of the whole body, and with higher efficacy and better pulmonary deposition rate of drug powder inhalation. Therefore, it is widely used in clinic [9]. However, local drug delivery is still limited by particle size, and whether it can reach deep lung tissue and small airway remains to be tested. At present, pulmonary function test is a widely used airway detection method, but children can rarely measure forced respiration well [10]. In addition, recent studies have found a low correlation between FEV1 and asthma symptoms [11]. Deep learning CT image algorithm for image reconstruction can recognize 99~199 μM structure, with very high resolution. Due to its noninvasive and intuitive characteristics, in recent years, it has been widely used in the research of children’s asthma.Deep learning CT images are simple, noninvasive, and do not require children to be able to comprehensively and intuitively observe and evaluate lung changes in asthma with small airway obstruction. Asthma with small airway obstruction has high clinical diagnosis and evaluation value. The purpose of this study was to evaluate the value of glucocorticoid in the treatment of asthma children with small airway obstruction.


## 2. Materials and Methods

### 2.1. Research Object

In this study, a total of 145 patients in hospital from March 2018 to November 2020 were included. Patients were randomly assigned to receive aerosolized glucocorticoid (n=45), aerosolized glucocorticoid combined with bronchodilator (n=50), or oral steroids (n=50) for 4 weeks after discharge. This study has been approved by the Medical Ethics Committee of Hospital, and the family members of patients included in the study have signed the consent.Inclusion criteria:(1) clinical symptoms and signs of children’s asthma: wheezing, shortness of breath, chest tightness or cough, physical and chemical irritation, upper respiratory tract infection, exercise, etc. were found repeatedly, and their diseases were excluded. (2) Bronchodilation test results were positive (absolute value of FEV1 increased by at least 199 mL), and the average daily change rate of expiratory peak flow exceeded 9% for 6 consecutive days. The above symptoms were met, and any objective airflow restriction test was performed to rule out wheezing, shortness of breath, chest tightness, and cough due to other diseases. (2) 5-15 years old with complete clinical data. (3) All patients received standardized treatment for 5-13 months according to the corresponding program of “Guidelines for Diagnosis and Treatment of Bronchial Asthma in Children.” (4) All asthma patients received standardized treatment for 5-13 months after clinical remission, and completed pulmonary ventilation function test; the patients with refractory asthma underwent lung ventilation function examination and lung CT examination at the same time (±3 weeks), and no asthma occurred during CT and lung ventilation function examination. Exclusion criteria: (1) patients with chronic lung disease, such as bronchopulmonary dysplasia, cystic fibrosis. (2) Other clear systemic diseases, such as immunodeficiency diseases. (3) A 3-week history of acute respiratory infection suspected of inhalation.


### 2.2. CT Scanning

Supine, natural inspiratory, 18-slice CT scan. Scanning parameters: 122 kV, 42 mA. Scanning range: lung apex to lung bottom. Scanning parameters: collimation17×0.78mm, layer thickness 1.1 mm, layer spacing 0.72 mm. Follow these instructions for CT scan.


### 2.3. Deep Learning Algorithm for Image Segmentation

The CT image was sent to the EBW workbench, and MPR, MIP, MINP, and other postprocessing were performed on the workbench. In the process of image processing, convolution operation was performed on each input pixel. The convolutional neural network first used the convolution kernel as the feature extractor to act on the original input matrix to create the first convolution layer. Then, on the basis of the convolution layer, the pooling operation was conducted, and the pooling layer was formed. Then, multiple convolution layers and overlapping pooling layers were established through continuous iteration, and multiple fully connected layers were formed to form a complete convolution neural network. Convolution neural network training was divided into forward propagation and backward propagation. In forward propagation, the sample matrix was entered by the first layer convolution network, and then the multilayer transformation flowed out, and the final output wasv’. Assuming the expected output of the sample was v, the error between the two is f. In backward propagation, the convolution kernel matrix was fine-tuned according to the principle of minimum error. The output of the v neurons in the i layer was set as follows.
(1)ngi=∑hvghibhi−2+agi.Among them,ngi represents the output of g neurons in i layer, bhi−2 represents the output of h neurons in i−2 layer, vghi represents the weight of the connection between g neurons and h neurons in i layer, and agi represents the correction term.
(2)bgi−2=σngi−3.σ is the activation function, Sigmiod function is rectifier linear function, unit (ReLU) function can be used, and the calculation requirements can be selected according to the actual situation. The error function can set the number according to the error between the expected value and the calculated value.
(3)ζ=ev,v′.E is a quadratic cost function, so the error of g neurons in i layer is as follows.
(4)δgi=∂ζ∂ngi.The error of the last layer of convolutional neural network is as follows.(5)δI=∇bιζΘσ′nI.Θ is the Hadamard product operator, ∇bιζ is the gradient value of the last layer, and the error of the other layers is as follows.
(6)δI=Vi+2pδI+2Θσni.The gradient descent method is used, and the convolution kernel formula can be obtained by equations (7) and (8).
(7)vi=vi−η∑yδy,iby,i−2P,(8)ai=ai−η∑yδy,i.Data analysis model of convolutional neural network is shown in Figure1.Figure 1
Data analysis model of convolutional neural network.


### 2.4. Grouping and Treatment Method

Patients with asthma accompanied by small airway injury were randomly divided into 3 groups. After discharge, all patients received aerosol inhalation of glucocorticoid, aerosol inhalation of glucocorticoid combined with bronchodilator therapy, and oral hormone maintenance therapy for 4 weeks. Lung function and FENO index were measured in each group to compare the efficacy and to explore the clinical effect of different glucocorticoid administration and combined glucocorticoid on small airway obstruction and airway inflammation in short-term maintenance therapy for acute exacerbation of asthma. (1) 145 patients with acute exacerbation of asthma were collected. General clinical data such as patient name, sex, course of disease, interval from onset, and routine medication were collected. (2) In the acute exacerbation stage, all patients were given anti-inflammatory, antiasthmatic, expectorant, and other treatments, with a course of about 8-13 days. Lung function indexes and FENO were detected after treatment until acute exacerbation symptoms were significantly improved. (3) Patients with airway obstruction (FEV1/FVC<70%, FEV1, FVC, PEF<80% expected value) and 12 small airway patients with normal airway function (FEV1, FVC, PEF all higher than 80% predicted value) were excluded according to pulmonary function test results. (4) Patients with small airway trauma were randomly divided into 3 groups in a double-blind randomized study: control group, combined administration group, and systemic administration group, who received budesonide suspension and micron (2.1 mL: LMG aerosol inhalation, 3 times a day, 2.2 mL: budesonide suspension: LMG combined with terbutaline sulfate aerosol, 2.2 mL: 5.2 mg aerosol inhalation, 3 times/day, oral methylprednisolone 7 mg/day, maintenance treatment for 4 weeks). (5) After 4 weeks of maintenance therapy, patients were asked to return to the hospital, and lung function and FENO indicators of patients in the control group, combined group, and systemic group were measured.


### 2.5. Observation Indicators

FENO levels maintained before and after 4 weeks in the control group, combined group, and systemic group were measured and recorded, forced expiratory flow 50%, FEF 50%, forced expiratory flow 75%, FEF 75%, and maximal mid-expiratory flow (MMEF 75/25). The improvement rates of FEF 50%, FEF 75%, MMEF 75/25, and FENO before and after maintenance treatment were calculated.Improvementrate=posttreatmentindex−pretreatmentindex/pretreatmentindex∗100%.


### 2.6. Statistical Analysis

SPSS21 was used to complete statistics. The statistical description of continuous variables was described bymean±standarddeviation of classification variables, and the classification variables were described by percentage. t test was used for the comparison of the mean values of two pairs of continuous variable data, analysis of variance was used for the comparison of the mean values of continuous variable data, and Chi-square test was used for the comparison of the mean values of two or more groups of classified variables. The difference was statistically significant (P<0.05).


## 2.1. Research Object

In this study, a total of 145 patients in hospital from March 2018 to November 2020 were included. Patients were randomly assigned to receive aerosolized glucocorticoid (n=45), aerosolized glucocorticoid combined with bronchodilator (n=50), or oral steroids (n=50) for 4 weeks after discharge. This study has been approved by the Medical Ethics Committee of Hospital, and the family members of patients included in the study have signed the consent.Inclusion criteria:(1) clinical symptoms and signs of children’s asthma: wheezing, shortness of breath, chest tightness or cough, physical and chemical irritation, upper respiratory tract infection, exercise, etc. were found repeatedly, and their diseases were excluded. (2) Bronchodilation test results were positive (absolute value of FEV1 increased by at least 199 mL), and the average daily change rate of expiratory peak flow exceeded 9% for 6 consecutive days. The above symptoms were met, and any objective airflow restriction test was performed to rule out wheezing, shortness of breath, chest tightness, and cough due to other diseases. (2) 5-15 years old with complete clinical data. (3) All patients received standardized treatment for 5-13 months according to the corresponding program of “Guidelines for Diagnosis and Treatment of Bronchial Asthma in Children.” (4) All asthma patients received standardized treatment for 5-13 months after clinical remission, and completed pulmonary ventilation function test; the patients with refractory asthma underwent lung ventilation function examination and lung CT examination at the same time (±3 weeks), and no asthma occurred during CT and lung ventilation function examination. Exclusion criteria: (1) patients with chronic lung disease, such as bronchopulmonary dysplasia, cystic fibrosis. (2) Other clear systemic diseases, such as immunodeficiency diseases. (3) A 3-week history of acute respiratory infection suspected of inhalation.


## 2.2. CT Scanning

Supine, natural inspiratory, 18-slice CT scan. Scanning parameters: 122 kV, 42 mA. Scanning range: lung apex to lung bottom. Scanning parameters: collimation17×0.78mm, layer thickness 1.1 mm, layer spacing 0.72 mm. Follow these instructions for CT scan.


## 2.3. Deep Learning Algorithm for Image Segmentation

The CT image was sent to the EBW workbench, and MPR, MIP, MINP, and other postprocessing were performed on the workbench. In the process of image processing, convolution operation was performed on each input pixel. The convolutional neural network first used the convolution kernel as the feature extractor to act on the original input matrix to create the first convolution layer. Then, on the basis of the convolution layer, the pooling operation was conducted, and the pooling layer was formed. Then, multiple convolution layers and overlapping pooling layers were established through continuous iteration, and multiple fully connected layers were formed to form a complete convolution neural network. Convolution neural network training was divided into forward propagation and backward propagation. In forward propagation, the sample matrix was entered by the first layer convolution network, and then the multilayer transformation flowed out, and the final output wasv’. Assuming the expected output of the sample was v, the error between the two is f. In backward propagation, the convolution kernel matrix was fine-tuned according to the principle of minimum error. The output of the v neurons in the i layer was set as follows.
(1)ngi=∑hvghibhi−2+agi.Among them,ngi represents the output of g neurons in i layer, bhi−2 represents the output of h neurons in i−2 layer, vghi represents the weight of the connection between g neurons and h neurons in i layer, and agi represents the correction term.
(2)bgi−2=σngi−3.σ is the activation function, Sigmiod function is rectifier linear function, unit (ReLU) function can be used, and the calculation requirements can be selected according to the actual situation. The error function can set the number according to the error between the expected value and the calculated value.
(3)ζ=ev,v′.E is a quadratic cost function, so the error of g neurons in i layer is as follows.
(4)δgi=∂ζ∂ngi.The error of the last layer of convolutional neural network is as follows.(5)δI=∇bιζΘσ′nI.Θ is the Hadamard product operator, ∇bιζ is the gradient value of the last layer, and the error of the other layers is as follows.
(6)δI=Vi+2pδI+2Θσni.The gradient descent method is used, and the convolution kernel formula can be obtained by equations (7) and (8).
(7)vi=vi−η∑yδy,iby,i−2P,(8)ai=ai−η∑yδy,i.Data analysis model of convolutional neural network is shown in Figure1.Figure 1
Data analysis model of convolutional neural network.


## 2.4. Grouping and Treatment Method

Patients with asthma accompanied by small airway injury were randomly divided into 3 groups. After discharge, all patients received aerosol inhalation of glucocorticoid, aerosol inhalation of glucocorticoid combined with bronchodilator therapy, and oral hormone maintenance therapy for 4 weeks. Lung function and FENO index were measured in each group to compare the efficacy and to explore the clinical effect of different glucocorticoid administration and combined glucocorticoid on small airway obstruction and airway inflammation in short-term maintenance therapy for acute exacerbation of asthma. (1) 145 patients with acute exacerbation of asthma were collected. General clinical data such as patient name, sex, course of disease, interval from onset, and routine medication were collected. (2) In the acute exacerbation stage, all patients were given anti-inflammatory, antiasthmatic, expectorant, and other treatments, with a course of about 8-13 days. Lung function indexes and FENO were detected after treatment until acute exacerbation symptoms were significantly improved. (3) Patients with airway obstruction (FEV1/FVC<70%, FEV1, FVC, PEF<80% expected value) and 12 small airway patients with normal airway function (FEV1, FVC, PEF all higher than 80% predicted value) were excluded according to pulmonary function test results. (4) Patients with small airway trauma were randomly divided into 3 groups in a double-blind randomized study: control group, combined administration group, and systemic administration group, who received budesonide suspension and micron (2.1 mL: LMG aerosol inhalation, 3 times a day, 2.2 mL: budesonide suspension: LMG combined with terbutaline sulfate aerosol, 2.2 mL: 5.2 mg aerosol inhalation, 3 times/day, oral methylprednisolone 7 mg/day, maintenance treatment for 4 weeks). (5) After 4 weeks of maintenance therapy, patients were asked to return to the hospital, and lung function and FENO indicators of patients in the control group, combined group, and systemic group were measured.


## 2.5. Observation Indicators

FENO levels maintained before and after 4 weeks in the control group, combined group, and systemic group were measured and recorded, forced expiratory flow 50%, FEF 50%, forced expiratory flow 75%, FEF 75%, and maximal mid-expiratory flow (MMEF 75/25). The improvement rates of FEF 50%, FEF 75%, MMEF 75/25, and FENO before and after maintenance treatment were calculated.Improvementrate=posttreatmentindex−pretreatmentindex/pretreatmentindex∗100%.


## 2.6. Statistical Analysis

SPSS21 was used to complete statistics. The statistical description of continuous variables was described bymean±standarddeviation of classification variables, and the classification variables were described by percentage. t test was used for the comparison of the mean values of two pairs of continuous variable data, analysis of variance was used for the comparison of the mean values of continuous variable data, and Chi-square test was used for the comparison of the mean values of two or more groups of classified variables. The difference was statistically significant (P<0.05).


## 3. Results

### 3.1. Basic Information of Patients

A total of 145 patients with bronchial asthma were included in this study. After treatment in acute exacerbation stage, 3 cases of respiratory failure, 28 cases of airway injury, and 14 cases of normal small airway function were excluded. Comparing and analyzing the baseline data of patients with small airway obstruction before maintenance treatment, there was no significant difference in gender, pulmonary function, and FENO among the three groups (P>0.05), as shown in Table 1 and Figure 2.Table 1
Basic data of patients.
VariableGroup (n=145)FPInhaled hormone (n=45)Combined inhalation (n=50)Oral hormone (n=50)Male2325240.410.65Dosing interval8.86±5.66.83±4.438.65±7.210.850.42FEF 502.3±0.482.18±0.362.39±0.340.510.43FEF 751.17±0.171.16±0.181.06±0.112.200.11MMEF 75/252.03±0.502.03±0.471.88±0.450.630.37FENO34.08±2.0334.035±2.0733.7±10.290.63Figure 2
Comparison of basic data of patients.


### 3.2. Evaluation of CT Image Segmentation Effect Based on Artificial Intelligence Segmentation Algorithm

It is very important for doctors to make surgical plans with the help of simulation system, formulate reasonable surgical plans, and reduce hands, so as to select the best surgical path and reduce injury, and improve the accuracy of tumor location, the complexity of surgery, and the success rate of surgery. The main technical indexes are the automatic segmentation accuracy is greater than 79%, the reconstruction accuracy of multidimensional visible liver can be observed at any angle, and the reconstruction accuracy is not lower than the original input CT resolution. The calculation error is less than 9%, and the volume of whole lung, lung segment, and residual lung is less than 9%. The following figure is the result of the segmentation of the target region in the CT image based on the CT image of the lung and the algorithm (Figure3).Figure 3
CT image of lung and result of target region segmentation in CT image by algorithm.


### 3.3. Comparison of Small Airway Function after Treatment

After 4 weeks of maintenance treatment, compared with FEF 50%, FEF 75%, MMEF 75/25 increased significantly, and FENO decreased significantly (P<0.05), as shown in Figure 4.Figure 4
Comparison of indicators before and after treatment. (a) is FEF 50%, (b) is FEF 75%, (c) is MME 75/25, (d) is FENO, and∗P<0.05.
(a)(b)(c)(d)


### 3.4. Comparison of Small Airway Function after Treatment

After maintenance treatment, the improvement results of FEF 50%, FEF 75%, MMEF 75/25, and FENO were as follows: the oral hormone group had the most significant improvement, followed by the combined atomization inhalation group, and the hormone atomization inhalation group improved the least. The difference among the groups was statistically significant (P<0.05), as shown in Figure 5.Figure 5
Comparison of therapeutic effects. (a) is FEF 50, (b) is FEF 75, (c) is MMEF 75/25, (d) is FENO, (e) is improvement rate, and∗P<0.05.
(a)(b)(c)(d)(e)


## 3.1. Basic Information of Patients

A total of 145 patients with bronchial asthma were included in this study. After treatment in acute exacerbation stage, 3 cases of respiratory failure, 28 cases of airway injury, and 14 cases of normal small airway function were excluded. Comparing and analyzing the baseline data of patients with small airway obstruction before maintenance treatment, there was no significant difference in gender, pulmonary function, and FENO among the three groups (P>0.05), as shown in Table 1 and Figure 2.Table 1
Basic data of patients.
VariableGroup (n=145)FPInhaled hormone (n=45)Combined inhalation (n=50)Oral hormone (n=50)Male2325240.410.65Dosing interval8.86±5.66.83±4.438.65±7.210.850.42FEF 502.3±0.482.18±0.362.39±0.340.510.43FEF 751.17±0.171.16±0.181.06±0.112.200.11MMEF 75/252.03±0.502.03±0.471.88±0.450.630.37FENO34.08±2.0334.035±2.0733.7±10.290.63Figure 2
Comparison of basic data of patients.


## 3.2. Evaluation of CT Image Segmentation Effect Based on Artificial Intelligence Segmentation Algorithm

It is very important for doctors to make surgical plans with the help of simulation system, formulate reasonable surgical plans, and reduce hands, so as to select the best surgical path and reduce injury, and improve the accuracy of tumor location, the complexity of surgery, and the success rate of surgery. The main technical indexes are the automatic segmentation accuracy is greater than 79%, the reconstruction accuracy of multidimensional visible liver can be observed at any angle, and the reconstruction accuracy is not lower than the original input CT resolution. The calculation error is less than 9%, and the volume of whole lung, lung segment, and residual lung is less than 9%. The following figure is the result of the segmentation of the target region in the CT image based on the CT image of the lung and the algorithm (Figure3).Figure 3
CT image of lung and result of target region segmentation in CT image by algorithm.


## 3.3. Comparison of Small Airway Function after Treatment

After 4 weeks of maintenance treatment, compared with FEF 50%, FEF 75%, MMEF 75/25 increased significantly, and FENO decreased significantly (P<0.05), as shown in Figure 4.Figure 4
Comparison of indicators before and after treatment. (a) is FEF 50%, (b) is FEF 75%, (c) is MME 75/25, (d) is FENO, and∗P<0.05.
(a)(b)(c)(d)


## 3.4. Comparison of Small Airway Function after Treatment

After maintenance treatment, the improvement results of FEF 50%, FEF 75%, MMEF 75/25, and FENO were as follows: the oral hormone group had the most significant improvement, followed by the combined atomization inhalation group, and the hormone atomization inhalation group improved the least. The difference among the groups was statistically significant (P<0.05), as shown in Figure 5.Figure 5
Comparison of therapeutic effects. (a) is FEF 50, (b) is FEF 75, (c) is MMEF 75/25, (d) is FENO, (e) is improvement rate, and∗P<0.05.
(a)(b)(c)(d)(e)


## 4. Discussion

In recent years, medical image segmentation has great application prospects in many medical fields. Childhood asthma is a “bad child condition” that predicts pulmonary function and airflow limitation in early adulthood. First, the related decline in pulmonary function may begin in infancy or fetus and continue to childhood and adulthood [12]. According to newspaper reports, asthma is associated with persistent low pulmonary function. People with a history of asthma have a positive risk of developing chronic obstructive pulmonary disease, which is 9 to 29 times higher than that of normal people [13]. A cohort study of COPD in an asthmatic population showed that the previous controls were asthma children and middle-aged people (OR: 2.87, P<0.001). FEV1/FVC was independently associated with airflow obstruction of smoking, which was lower than that of nonsmokers [14]. Under the effect of pulmonary function, the complete control of asthma children and middle-aged people can be realized. One of the mechanisms is that subclinical airway inflammation and airway hyperreactive asthma persist after remission in children, resulting in the rapid decline of FEV1 and the late airflow obstruction [15]. The second possible mechanism is that childhood asthma limits lung growth. After measuring the thickness of reticular basement membrane, children with severe asthma may have airway remodeling [16, 17]. Patients with poor basic lung function and increased eosinophils and cells in blood tend to aggravate the acute attack. Thickening of bronchial wall is the most common in chest CT of asthmatic children (41.51%) [18]. In order to reduce the clinical symptoms, physiological, and economic burden of acute asthma attack, improve the airway remodeling and airway inflammation, improve the quality of life, and prolong the survival time of asthmatic patients, glucocorticoids were used to treat asthma children complicated with small airway obstruction, and CT images were used to evaluate the therapeutic effect. The results showed that other imaging manifestations were mosaic sign, nodular shadow, and consolidation, and there were no signs of interlobular septal thickening, reticular lattice shadow, and honeycomb shadow. There were no signs of infection in pulmonary function examination and lung CT, so the possible status of airway remodeling was considered.FEF 50% is an indicator of midexpiratory flow, which is involved in the judgment of small airway dysfunction together with MMEF and FEF75%. Two or more of these three indicators declined, reflecting airway obstruction or small airway lesions. FEF 75% is an indicator of late expiratory flow, and MMEF75/25 is an indicator of small airway function. Exhaled nitric oxide (FeNO) can directly reflect airway inflammation, predict the response of inhaled corticosteroids (ICS), and guide the treatment of hormone degradation. Airway remodeling is a significant pathological feature of asthma. It has long been considered to be the result of repeated acute and chronic inflammation. The onset of asthma not only involves the airway but also leads to small airway obstruction and airflow restriction [19]. In normal people, the airway resistance is relatively small, less than 20% of the total lung resistance. However, in asthmatic patients, the pathological characteristics of airway resistance are chronic airway inflammation, increased mucus secretion, and inflammatory cell infiltration, which are higher than those in healthy people. Most patients with asthma may have small airway involvement in the early stage. Usually, the effect of dredging airway obstruction is slow during treatment. Both China GINA guidelines and asthma diagnosis and treatment guidelines use atmospheric beam index for diagnosis and monitoring. The deep learning CT imaging tool has good spatial resolution and can clearly display the fine structure of lung tissue. Its morphology is almost the same as that of gross specimens. In addition, small airway obstruction in asthma is often closely related to some specific types of asthma [20, 21]. In this study, glucocorticoids were used in the treatment of asthma children with small airway obstruction, and the effects of oral hormone, combined atomization inhalation, and hormone atomization inhalation on children’s lung function were evaluated. The results showed that different treatment methods could improve the level of nitric oxide, the instantaneous flow rate at 50% forced expiratory, the instantaneous flow rate at 75% forced expiratory, and the maximum midexpiratory flow rate, but the effect of hormone atomization inhalation was the most significant. It shows that the treatment of asthma children with and small airway function obstruction through glucocorticoid atomization inhalation can have a better clinical therapeutic effect [22].


## 5. Conclusion

The more severe the peripheral airway injury of asthma children with small airway function injury, the longer the time interval from onset to regular medication. It is informed that we can use control drugs regularly in the early stage after the onset of asthma to control the process of small airway obstruction. After acute exacerbation of asthma in childhood, glucocorticoids can improve small airway function and airway inflammation. Systemic application of hormone is more effective in improving small airway function and airway inflammation than local medication. In aerosol inhalation therapy, hormone combined with bronchodilator can better improve small airway obstruction and reduce airway inflammation than single drug inhalation. In this study, the sample size is small, and more experimental people should be included. Clinical trials should be conducted in multicenter and large sample hospitals instead of in a single area or small area. This study can intuitively observe and evaluate the pulmonary changes of asthma with small airway functional obstruction. Asthma with small airway functional obstruction has higher clinical diagnosis and evaluation value.



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