Background: Dental development assessment is an important factor in dental age estimation and dental maturity evaluation. This study aimed to develop and evaluate the performance of an automated dental development staging system based on Demirjian’s method using deep learning. Methods: The study included 5133 anonymous panoramic radiographs obtained from the Department of Pediatric Dentistry database at Seoul National University Dental Hospital between 2020 and 2021. The proposed methodology involves a three-step procedure for dental staging: detection, segmentation, and classification. The panoramic data were randomly divided into training and validating sets (8:2), and YOLOv5, U-Net, and EfficientNet were trained and employed for each stage. The models’ performance, along with the Grad-CAM analysis of EfficientNet, was evaluated. Results: The mean average precision (mAP) was 0.995 for detection, and the segmentation achieved an accuracy of 0.978. The classification performance showed F1 scores of 69.23, 80.67, 84.97, and 90.81 for the Incisor, Canine, Premolar, and Molar models, respectively. In the Grad-CAM analysis, the classification model focused on the apical portion of the developing tooth, a crucial feature for staging according to Demirjian’s method. Conclusions: These results indicate that the proposed deep learning approach for automated dental staging can serve as a supportive tool for dentists, facilitating rapid and objective dental age estimation and dental maturity evaluation.

There are many types of disabilities, and each type has a variety related to socioeconomic factors. Such factors affect to many health problems of the disabled. However, surveys of the oral health status of the disabled in Korea are rare. The purpose of this study was to estimate oral health disparity through comparing oral health status of the disabled to the non-disabled, adjusted for the net effect of the disability on oral health status. A cross-sectional study was conducted among the disabled in urban and suburban areas in Korea from June to September 2016. People with physical, mental, and multiple disabilities took part in this survey. The clinical examinations were carried out by trained dentists. Statistical analysis was performed to quantify the association between oral health and socioeconomic status after restricting the analysis using a propensity score matching method. The disabled had more DMFT, DT, and MT, fewer FT, and fewer teeth than the non-disabled based on entire groups (P<0.01). No difference in the ratio of periodontitis was observed. The subjects with mental disabilities (MD) scored 3.09 (95% CI, 1.07–8.97), and those with multiple disabilities scored 4.37 (95% CI, 1.16–16.37) for edentulous status. The MD had an odds ratio of 1.34 (95% CI, 1.03–1.74), and those with multiple disabilities had an odds ratio of 1.75 (95% CI, 1.11–2.76) for the DMFT index. These results represent poor oral health status of the disabled compared to the non-disabled. Consequentially, we can verify that not only the existence of disability but also the type of disability has a decisive effect on oral health condition. This comparison is necessary to widen our approach to evaluate the actual status condition of the disabled.