ABSTRACT
Purpose@#We aimed to investigate the prevalences of obesity, abdominal obesity, and non-alcoholic fatty liver disease (NAFLD) among children and adolescents during the coronavirus disease 2019 (COVID-19) outbreak. @*Materials and Methods@#This population-based study investigated the prevalences of obesity, abdominal obesity, and NAFLD among 1428 children and adolescents between 2018–2019 and 2020. We assessed the prevalences of obesity, abdominal obesity, and NAFLD according to body mass index, age, sex, and residential district. Logistic regression analyses were performed to determine the relationships among obesity, abdominal obesity, and NAFLD. @*Results@#In the obese group, the prevalence of abdominal obesity increased from 75.55% to 92.68%, and that of NAFLD increased from 40.68% to 57.82%. In age-specific analysis, the prevalence of abdominal obesity increased from 8.25% to 14.11% among participants aged 10–12 years and from 11.70% to 19.88% among children aged 13–15 years. In residential district-specific analysis, the prevalence of both abdominal obesity and NAFLD increased from 6.96% to 15.74% in rural areas. In logistic regression analysis, the odds ratio of abdominal obesity for NAFLD was 11.82. @*Conclusion@#Our results demonstrated that the prevalences of abdominal obesity and NAFLD increased among obese Korean children and adolescents and in rural areas during the COVID-19 outbreak. Additionally, the prevalence of abdominal obesity increased among young children. These findings suggest the importance of closely monitoring abdominal obesity and NAFLD among children during COVID-19, focusing particularly on obese young children and individuals in rural areas.
ABSTRACT
Purpose@#The appropriate evaluation of height and accurate estimation of bone age are crucial for proper assessment of the growth status of a child. We developed a bone age estimation program using a deep learning algorithm and established a model to predict the final adult height of Korean children. @*Materials and Methods@#A total of 1678 radiographs from 866 children, for which the interpretation results were consistent between two pediatric endocrinologists, were used to train and validate the deep learning model. The bone age estimation algorithm was based on the convolutional neural network of the deep learning system. The test set simulation was performed by a deep learning program and two raters using 150 radiographs and final height data for 100 adults. @*Results@#There was a statistically significant correlation between bone age interpreted by the artificial intelligence (AI) program and the reference bone age in the test set simulation (r=0.99, p<0.001). In the test set simulation, the AI program showed a mean absolute error (MAE) of 0.59 years and a root mean squared error (RMSE) of 0.55 years, compared with reference bone age, and showed similar accuracy to that of an experienced pediatric endocrinologist (rater 1). Prediction of final adult height by the AI program showed an MAE of 4.62 cm, compared with the actual final adult height. @*Conclusion@#We developed a bone age estimation program based on a deep learning algorithm. The AI-derived program demonstrated high accuracy in estimating bone age and predicting the final adult height of Korean children and adolescents.
ABSTRACT
Kawasaki disease (KD) is a common febrile disease in East Asia. Because KD with coronary artery aneurysm (CAA) may predispose to thrombosis, children with KD-associated CAA may need anticoagulation in addition to aspirin. In this report, we describe a 6-year-old girl with KD and CAA who was found to have unexpected warfarin-induced coagulopathy caused by CYP2C9 and VKORC1 genotype variants, which affect warfarin metabolism.
ABSTRACT
Kawasaki disease (KD) is a common febrile disease in East Asia. Because KD with coronary artery aneurysm (CAA) may predispose to thrombosis, children with KD-associated CAA may need anticoagulation in addition to aspirin. In this report, we describe a 6-year-old girl with KD and CAA who was found to have unexpected warfarin-induced coagulopathy caused by CYP2C9 and VKORC1 genotype variants, which affect warfarin metabolism.