ABSTRACT
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.
Subject(s)
Age Determination by Teeth , Deep Learning , Child , Humans , Radiography, Panoramic , Age Determination by Teeth/methods , Incisor , MolarABSTRACT
BACKGROUND: The correlation between dental maturity and skeletal maturity has been proposed, but its clinical application remains challenging. Moreover, the varying correlations observed in different studies indicate the necessity for research tailored to specific populations. AIM: To compare skeletal maturity in Korean children with advanced and delayed dental maturity using dental maturity percentile. DESIGN: Dental panoramic radiographs and cephalometric radiographs were obtained from 5133 and 395 healthy Korean children aged between 4 and 16 years old. Dental maturity was assessed with Demirjian's method, while skeletal maturity was assessed with the cervical vertebral maturation method. Standard percentile curves were developed through quantile regression. Advanced (93 boys and 110 girls) and delayed (92 boys and 100 girls) dental maturity groups were defined by the 50th percentile. RESULTS: The advanced group showed earlier skeletal maturity in multiple cervical stages (CS) in both boys (CS 1, 2, 3, 4, and 6) and girls (CS 1, 3, 4, 5, and 6). Significant differences, as determined by Mann-Whitney U tests, were observed in CS 1 for boys (p = 0.004) and in CS 4 for girls (p = 0.037). High Spearman correlation coefficients between dental maturity and cervical vertebral maturity exceeded 0.826 (p = 0.000) in all groups. CONCLUSION: A correlation between dental and skeletal maturity, as well as advanced skeletal maturity in the advanced dental maturity group, was observed. Using percentile curves to determine dental maturity may aid in assessing skeletal maturity, with potential applications in orthodontic diagnosis and treatment planning.
Subject(s)
Age Determination by Teeth , Adolescent , Child , Child, Preschool , Female , Humans , Male , Age Determination by Teeth/methods , Radiography, Panoramic , Republic of Korea , Retrospective Studies , East Asian PeopleABSTRACT
BACKGROUND/PURPOSE: Glass ionomers undergo degradation when exposed to fluoride, which changes the physico-chemical characteristics of the materials. The purpose of this study was to evaluate the surface changes of resin-modified glass ionomer (RMGI) when immersed in a sodium fluoride (NaF) solution according to pH and time. MATERIALS AND METHODS: 120 RMGI specimens were prepared, and 30 specimens were placed in four types of storage solutions for four weeks; pH 7 artificial saliva with or without 0.2% NaF (As7 and NaF7), pH 5 artificial saliva with or without 0.2% NaF (As5 and NaF5). Interferometry and microscopy were performed to evaluate the surface roughness and topography, while spectroscopy was used to analyze the chemical composition changes. RESULTS: Rougher topography and increased roughness was exhibited in NaF groups, owing to the disintegration of the polysalt matrix. Reduced Sr and F was exhibited in all groups, whereas NaF group showed a decrease in Al and inorganic components. CONCLUSION: This study suggest that excessive use of fluoride therapy could lead to severe degradation of RMGI.
