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.

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%.

Purpose: In the present study, we determined the prevalence of obstructive meibomian gland dysfunction (MGD), hyposecretory MGD, grossly normal MG, and hypersecretory MGD in patients with dry eye syndrome using lipid layer thickness (LLT) and MG dropout. Methods: Eighty-eight patients with dry eye syndrome were included in the study. Patients were categorized into four groups according to the LLT and weighted total meiboscore. The proportion of patients in each group was calculated. The age, sex, Ocular Surface Disease Index, LLT, Schirmer, tear film breakup time, cornea stain, weighted total meiboscore, expressibility, and quality of meibum were compared between the four groups. Results: Fifteen eyes (17.0%) had obstructive MGD, two eyes (2.3%) had hyposecretory MGD, 40 eyes (45.5%) had grossly normal MG, and 17 eyes (19.3%) had hypersecretory MGD. The obstructive MGD group was younger than the grossly normal MG group. In obstructive MGD, the ratio of men to women was higher than that of the other groups. However, Ocular Surface Disease Index, Schirmer, tear film breakup time, and corneal stain did not show statistically significant differences between the four groups. The meibum expressibility of the hyposecretoy MGD group was worse than those of the other groups. The meibum expressibility of the hyposecretoy MGD group was poor than those of the obstructive and hypersecretory MGD group. Conclusions This categorization was expected to help determine the best treatment method for dry eye syndrome, according to the MG status.