The purpose of this study was to investigate pediatric emergency department visits patterns and characteristics of children and adolescents under the age of 20 in South Korea from 2002 to 2015 due to dental conditions. This study used a stratified sample of approximately 1 million people from the Health Insurance Review and Assessment Service Database. The age, region, household income, and treatment cost were included for the patient characteristic analysis. Pediatric patients were compared to the adult group (over 20 years old).In children and adolescents, the rate of emergency department visits due to dental conditions was higher for traumatic conditions than for non-traumatic conditions. Children and adolescents with higher household income visited the emergency department more often than those with lower household income. The region with the highest number of children and adolescents visiting the emergency department for dental conditions was Busan (per 100,000 population).Although this study could not confirm the annual trend of children and adolescents’ dental emergency visits due to the sample size limitation, the characteristics of children and adolescents’ dental emergency visits were compared with those of adults using a stratified sample.

The aim of this study was to evaluate the use of easily accessible machine learning application to identify mesiodens, and to compare the ability to identify mesiodens between trained model and human.
A total of 1604 panoramic images (805 images with mesiodens, 799 images without mesiodens) of patients aged 5 – 7 years were used for this study. The model used for machine learning was Google’s teachable machine. Data set 1 was used to train model and to verify the model. Data set 2 was used to compare the ability between the learning model and human group.
As a result of data set 1, the average accuracy of the model was 0.82. After testing data set 2, the accuracy of the model was 0.78. From the resident group and the student group, the accuracy was 0.82, 0.69.
This study developed a model for identifying mesiodens using panoramic radiographs of children in primary and early mixed dentition. The classification accuracy of the model was lower than that of the resident group. However, the classification accuracy (0.78) was higher than that of dental students (0.69), so it could be used to assist the diagnosis of mesiodens for non-expert students or general dentists.