Purpose: Magnetic resonance imaging (MRI) can be used for assessing the morphology of the pituitary gland in children with short stature. The purposes of this study were: (1) to determine if pituitary volume (PV) can distinguish patients with growth hormone (GH) deficiency from those with idiopathic short stature (ISS), (2) to validate an association between PV and severity of GH deficiency, and (3) to compare PV between good and poor response groups in children with GH deficiency or ISS after 1 year of treatment. Methods: Data were collected from the medical records of 152 children with GH deficiency or ISS who underwent GH stimulation test, sella MRI, and GH treatment for at least 1 year. Estimated PVs were calculated using the formula of an ellipsoid. We compared the PVs in patients with GH deficiency with those of patients with ISS. In addition, we assessed the association between PV and severity of GH deficiency, and we assessed growth response after treatment. Results: No difference was observed in PV between patients with GH deficiency and those with ISS. The severity of the GH deficiency seemed to be associated with PV (P=0.082), and the height of the pituitary gland was associated with severity of GH deficiency (P<0.005). The PV in the good response group was less than that of the poor response group in patients with GH deficiency (P<0.005), and PV showed no association with responsiveness to GH treatment in patients with ISS (P=0.073). Conclusion The measurement of PV cannot be used for differential diagnosis between GH deficiency and ISS. In patients with GH deficiency, PV tended to be smaller as the severity of GH deficiency increased, but the difference was not significant. PV may be a good response predictor for GH treatment. Further studies, including a radiomics-based approach, will be helpful in elucidating the clinical implications of pituitary morphology in patients with short stature.

Purpose: Magnetic resonance imaging (MRI) can be used for assessing the morphology of the pituitary gland in children with short stature. The purposes of this study were: (1) to determine if pituitary volume (PV) can distinguish patients with growth hormone (GH) deficiency from those with idiopathic short stature (ISS), (2) to validate an association between PV and severity of GH deficiency, and (3) to compare PV between good and poor response groups in children with GH deficiency or ISS after 1 year of treatment. Methods: Data were collected from the medical records of 152 children with GH deficiency or ISS who underwent GH stimulation test, sella MRI, and GH treatment for at least 1 year. Estimated PVs were calculated using the formula of an ellipsoid. We compared the PVs in patients with GH deficiency with those of patients with ISS. In addition, we assessed the association between PV and severity of GH deficiency, and we assessed growth response after treatment. Results: No difference was observed in PV between patients with GH deficiency and those with ISS. The severity of the GH deficiency seemed to be associated with PV (P=0.082), and the height of the pituitary gland was associated with severity of GH deficiency (P<0.005). The PV in the good response group was less than that of the poor response group in patients with GH deficiency (P<0.005), and PV showed no association with responsiveness to GH treatment in patients with ISS (P=0.073). Conclusion The measurement of PV cannot be used for differential diagnosis between GH deficiency and ISS. In patients with GH deficiency, PV tended to be smaller as the severity of GH deficiency increased, but the difference was not significant. PV may be a good response predictor for GH treatment. Further studies, including a radiomics-based approach, will be helpful in elucidating the clinical implications of pituitary morphology in patients with short stature.

Purpose: Magnetic resonance imaging (MRI) can be used for assessing the morphology of the pituitary gland in children with short stature. The purposes of this study were: (1) to determine if pituitary volume (PV) can distinguish patients with growth hormone (GH) deficiency from those with idiopathic short stature (ISS), (2) to validate an association between PV and severity of GH deficiency, and (3) to compare PV between good and poor response groups in children with GH deficiency or ISS after 1 year of treatment. Methods: Data were collected from the medical records of 152 children with GH deficiency or ISS who underwent GH stimulation test, sella MRI, and GH treatment for at least 1 year. Estimated PVs were calculated using the formula of an ellipsoid. We compared the PVs in patients with GH deficiency with those of patients with ISS. In addition, we assessed the association between PV and severity of GH deficiency, and we assessed growth response after treatment. Results: No difference was observed in PV between patients with GH deficiency and those with ISS. The severity of the GH deficiency seemed to be associated with PV (P=0.082), and the height of the pituitary gland was associated with severity of GH deficiency (P<0.005). The PV in the good response group was less than that of the poor response group in patients with GH deficiency (P<0.005), and PV showed no association with responsiveness to GH treatment in patients with ISS (P=0.073). Conclusion The measurement of PV cannot be used for differential diagnosis between GH deficiency and ISS. In patients with GH deficiency, PV tended to be smaller as the severity of GH deficiency increased, but the difference was not significant. PV may be a good response predictor for GH treatment. Further studies, including a radiomics-based approach, will be helpful in elucidating the clinical implications of pituitary morphology in patients with short stature.

Purpose: Metabolic syndrome (MetS) comprises a cluster of risk factors for future cardiovascular and metabolic diseases. Only a few recent studies have reported the trend in the prevalence of MetS in youth. This study aimed to analyze trends in the prevalence of MetS and nutrient intake in the last 10 years and investigate the changes in MetS components among Korean children and adolescents. Materials and Methods: We analyzed the data of 9513 children and adolescents aged 10–19 years from the 2008–2017 Korean National Health and Nutrition Examination Surveys. Diagnosis of MetS was based on the International Diabetes Federation (IDF) and modified National Cholesterol Education Program-Adult Treatment Panel III (NCEP-ATP III) criteria. Results: Based on the IDF criteria, MetS prevalence increased from 1.53% in 2008 to 3.19% in 2017 (p=0.007). Based on the NCEPATP III criteria, MetS prevalence increased from 2.18% in 2008 to 3.19% in 2017; however, the increase was not statistically significant. Daily calorie and fat intakes increased significantly during the study period. Among the risk factors that MetS comprises, the prevalence rates of central obesity, low high-density lipoprotein cholesterol levels, and high fasting glucose levels increased significantly. Conclusion Over the last 10 years, the prevalence of MetS has grown significantly with increasing calorie and fat intake in Korean children and adolescents. Central obesity and high-density lipoprotein cholesterol and fasting glucose levels have worsened.Therefore, active support and close monitoring are required to control MetS and prevent further increase in the prevalence of cardiovascular diseases.

Objectives: With the rapid changes in society, there are more arguments for redefining dental education system. This study aimed to review and analyze the problems in the current dental education system in Korea and then propose an integrated education system based on those findings. Methods: A narrative review of the problems confronting the dental education system in Korea was carried out. Accordingly, full-text screening and specialists’ discussions were extracted from the review. The purpose of the review was to summarize the strengths and weaknesses of Korean dental education systems to offer perspectives on an integrated education system in accordance with the specialists’ opinions. Results: The results revealed that the problems of the current dental education system were identified in clinical education, basic science, and humanities and social sciences. Dental schools should provide clinical education so that dental students can be exposed to the clinical environment at an early stage and perform patient-based clinical practice. It is also imperative to encourage participation in research programs to train dental scientists, as well as to reflect on courses that can adapt to changes brought about by new technologies. It is recommended that education opportunities for integrated humanities and social sciences be provided. Conclusions It will be critical in the future for dental schools to embrace a flexible education system from various perspectives. It is vital that dental schools play a role in developing students’ critical thinking, problem-solving, and decision-making skills. Finally, as part of an integrated education system, an improvement program should be devised.

Objectives: With the rapid changes in society, there are more arguments for redefining dental education system. This study aimed to review and analyze the problems in the current dental education system in Korea and then propose an integrated education system based on those findings. Methods: A narrative review of the problems confronting the dental education system in Korea was carried out. Accordingly, full-text screening and specialists’ discussions were extracted from the review. The purpose of the review was to summarize the strengths and weaknesses of Korean dental education systems to offer perspectives on an integrated education system in accordance with the specialists’ opinions. Results: The results revealed that the problems of the current dental education system were identified in clinical education, basic science, and humanities and social sciences. Dental schools should provide clinical education so that dental students can be exposed to the clinical environment at an early stage and perform patient-based clinical practice. It is also imperative to encourage participation in research programs to train dental scientists, as well as to reflect on courses that can adapt to changes brought about by new technologies. It is recommended that education opportunities for integrated humanities and social sciences be provided. Conclusions It will be critical in the future for dental schools to embrace a flexible education system from various perspectives. It is vital that dental schools play a role in developing students’ critical thinking, problem-solving, and decision-making skills. Finally, as part of an integrated education system, an improvement program should be devised.

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.