Prevalence and Current Status of Dental Treatment for Amelogenesis Imperfecta and Dentinogenesis Imperfecta using National Health Insurance Database / 대한소아치과학회지

The aim of this study was to determine the prevalence and incidence and evaluate the current status of dental treatment of Amelogenesis imperfecta (AI) and Dentinogenesis imperfecta (DI) in South Korea. The data was based on National Health Insurance Service (NHIS)-National Sample Cohort Database (2002 - 2015) and Jeonbuk National University (JBNU) Dental Hospital (2011 - 2020).The NHIS data analysis showed prevalence of AI and DI were 11.6 and 2.4 per 100,000 people, respectively. The annual incidence of AI and DI for 2013 - 2015 were 2.2 and 0.5 per 100,000. There were no statistically significant differences regarding the number of visits, the reimbursable cost among AI, DI patients and others.In the patient analysis of the JBNU dental hospital, proportion of the reimbursable and non-reimbursable cost for AI patients were 12.1% and 87.9%, while DI patients accounted for 18.6% and 81.4%.

Identification of Mesiodens Using Machine Learning Application in Panoramic Images / 대한소아치과학회지

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.