OBJECTIVES: To investigate the current status and influencing factors of sleep disorders in school-age children with asthma, providing a scientific basis for improving sleep quality and quality of life of asthmatic children. METHODS: This study selected school-age children with asthma admitted to the Children's Hospital of Nanjing Medical University from March 2022 to March 2024 as subjects. A questionnaire was used to assess their sleep conditions, and based on the assessment results, the participants were divided into a sleep disorder group (106 children) and a non-sleep disorder group (181 children). Multivariate logistic regression analysis was conducted to identify the influencing factors of sleep disorders in asthmatic children. RESULTS: A total of 287 asthmatic children were included, of which 106 (36.9%) had sleep disorders. Multivariate logistic regression analysis showed that being older than 10 years, obesity, poor medication adherence, unhealthy family functioning, passive smoking, and participation in only some physical activities were all risk factors for sleep disorders in school-aged children with asthma (P<0.05). CONCLUSIONS The incidence of sleep disorders in school-age children with asthma is relatively high and influenced by multiple factors, including age, obesity, poor medication adherence, unhealthy family functioning, passive smoking, and limited participation in physical activities. To improve the sleep quality and quality of life of asthmatic children, corresponding intervention measures should be implemented targeting these influencing factors.
Humans ; Asthma/complications* ; Child ; Male ; Female ; Sleep Wake Disorders/etiology* ; Logistic Models ; Quality of Life ; Adolescent ; Risk Factors

Objective: To establish a comprehensive diagnostic classification model of lateral cephalograms based on artificial intelligence (AI) to provide reference for orthodontic diagnosis. Methods: A total of 2 894 lateral cephalograms were collected in Department of Orthodontics, Capital Medical University School of Stomatology from January 2015 to December 2021 to construct a data set, including 1 351 males and 1 543 females with a mean age of (26.4± 7.4) years. Firstly, 2 orthodontists (with 5 and 8 years of orthodontic experience, respectively) performed manual annotation and calculated measurement for primary classification, and then 2 senior orthodontists (with more than 20 years of orthodontic experience) verified the 8 diagnostic classifications including skeletal and dental indices. The data were randomly divided into training, validation, and test sets in the ratio of 7∶2∶1. The open source DenseNet121 was used to construct the model. The performance of the model was evaluated by classification accuracy, precision rate, sensitivity, specificity and area under the curve (AUC). Visualization of model regions of interest through class activation heatmaps. Results: The automatic classification model of lateral cephalograms was successfully established. It took 0.012 s on average to make 8 diagnoses on a lateral cephalogram. The accuracy of 5 classifications was 80%-90%, including sagittal and vertical skeletal facial pattern, mandibular growth, inclination of upper incisors, and protrusion of lower incisors. The acuracy rate of 3 classifications was 70%-80%, including maxillary growth, inclination of lower incisors and protrusion of upper incisors. The average AUC of each classification was ≥0.90. The class activation heat map of successfully classified lateral cephalograms showed that the AI model activation regions were distributed in the relevant structural regions. Conclusions: In this study, an automatic classification model for lateral cephalograms was established based on the DenseNet121 to achieve rapid classification of eight commonly used clinical diagnostic items.
Male ; Female ; Humans ; Young Adult ; Adult ; Artificial Intelligence ; Deep Learning ; Cephalometry ; Maxilla ; Mandible/diagnostic imaging*