Predicting chronological age of 14 or 18 in adolescents: integrating dental assessments with machine learning.

AIM: Age estimation plays a critical role in personal identification, especially when determining compliance with the age of consent for adolescents. The age of consent refers to the minimum age at which an individual is legally considered capable of providing informed consent for sexual activities. The purpose of this study is to determine whether adolescents meet the age of 14 or 18 by using dental development combined with machine learning. METHODS: This study combines dental assessment and machine learning techniques to predict whether adolescents have reached the consent age of 14 or 18. Factors such as the staging of the third molar, the third molar index, and the visibility of the periodontal ligament of the second molar are evaluated. RESULTS: Differences in performance metrics indicate that the posterior probabilities achieved by machine learning exceed 93% for the age of 14 and slightly lower for the age of 18. CONCLUSION: This study provides valuable insights for forensic identification for adolescents in personal identification, emphasizing the potential to improve the accuracy of age determination within this population by combining traditional methods with machine learning. It underscores the importance of protecting and respecting the dignity of all individuals involved.

Hsa_circ_0036872 has an important promotional effect in enhancing osteogenesis of dental pulp stem cells by regulating the miR-143-3p/IGF2 axis.

BACKGROUND: Circular RNAs (circRNAs), an extremely stable group of RNAs, possess a covalent closed-loop configuration. Numerous studies have highlighted the involvement of circRNAs in physiological processes and the development of various diseases. The present study aimed to investigate how circRNA regulates the osteogenic differentiation of human dental pulp stem cells (hDPSCs). METHODS: We isolated hDPSCs from dental pulp and used next-generation sequencing analysis to determine the differentially-expressed circRNAs during osteogenic differentiation. Bioinformatics and dual-luciferase reporter assays identified the downstream targets. The role of circRNAs in osteogenic differentiation was further confirmed through the use of heterotopic bone models. RESULTS: We found that hsa_circ_0036872 expression was increased during osteogenic differentiation of hDPSCs, and downregulation of hsa_circ_0036872 inhibited their osteogenic differentiation. Dual-luciferase reporter assays showed that both miR-143-3p and IGF2 were downstream targets of hsa_circ_0036872. Overexpression of IGF2 or inhibition of miR-143-3p restored the osteogenic differentiation ability of hDPSCs after silencing hsa_circ_0036872. Overexpression of IGF2 reversed the inhibitory effect of miR-143-3p on osteogenic differentiation. CONCLUSION: Taken together, our results show that hsa_circ_0036872 exerts an important promotional effect in enhancing the osteogenesis of dental pulp stem cells by regulating the miR-143-3p/IGF2 axis. These data suggest a novel therapeutic strategy for osteoporosis treatment and periodontal tissue regeneration.

Applicability of the London Atlas method in the East China population.

BACKGROUND: Dental age estimation is important for developmental assessment and individual identification. The London Atlas, a recently proposed method for dental age estimation, has been reported to perform satisfactorily in various populations. OBJECTIVE: In this study, we assessed the reproducibility, repeatability and applicability of the London Atlas method in the East China population and compared it with the Demirjian method. MATERIALS AND METHODS: We assessed panoramic radiographs of 835 pediatric patients ages 6.0-19.9 years using the London Atlas and the Demirjian method. We employed the intraclass correlation coefficient and Bland-Altman analysis to evaluate reproducibility and repeatability, respectively. We assessed the agreement between dental age and chronological age and calculated 95% and 80% prediction intervals for each dental age stage. Sensitivity, specificity and predictive values were calculated to assess the performance of both methods for identifying threshold ages. RESULTS: The London Atlas has better reproducibility and repeatability (intraclass correlation coefficients: 0.98 and 0.99; 95% limits of agreement: - 1.34 to 1.56 and - 1.22 to 0.88, respectively). Dental age estimated using the London Atlas was closer to chronological age in both genders (median absolute error = 0.58). The 95% prediction intervals for chronological age were wide (0.99 to 9.89 years). CONCLUSION: The London Atlas has excellent reproducibility and repeatability. Thus, it might offer an alternative method for developmental assessment. We observed considerable variation in dental development in the East China population, which needs further research.

Performance of the London Atlas, Willems, and a new quick method for dental age estimation in Chinese Uyghur children.

BACKGROUND: Numerous dental age estimation methods have been devised and practised for decades. Among these, the London Atlas and Willems methods were two of the most frequently adopted, however dependent on atlantes or tables. A new estimation method less reliant on external measurement could be efficient and economical. AIM: This study aimed to evaluate the utility and applicability of the dental age estimation methods of London Atlas, Willems, and a new quick method that subtracts the number of developing teeth from the universal root mature age of 16 years in one of the lower quadrants reported in this work among Chinese Uyghur children. METHODS: A comparative cross-sectional study was conducted. Subjects enrolled in the study were screened according to preset inclusion and exclusion criteria. The observer then obtained the dental age from the subjects' panoramic radiographs based on the estimated rules of the London Atlas, Willems, and a new quick method. Paired t-test was used to compare the accuracy and precision of the above three estimation methods. Independent-sample t-test was used to find the difference between gender. RESULTS: Totally, 831 radiographs entered the analyses of this study. Among the three methods evaluated, the Willems method, in particular, showed a distinct underestimated tendency. The mean error of the dental age predicted by the London Atlas, the Willems method, and the quick method was 0.06 ± 1.13 years, 0.44 ± 1.14 years, and 0.30 ± 0.63 years, respectively. The mean absolute error was 0.86 ± 0.75 years according to the London Atlas, 1.17 ± 0.89 years under the Willems method, and 0.70 ± 0.54 years under our quick method. No significant difference was found between the chronological age and dental age using the London Atlas, generally for the 10 to 15 years group (p > 0.05), but our quick method for the 15-16 years children (p < 0.05) and Willems method (p < 0.001). CONCLUSION: The London Atlas outperformed the Willems method with better accuracy and precision among 10-15 years Chinese Uyghur children. Our new quick method may be comparable to the London Atlas for children aged 10-14 and potentially become a more straightforward dental age prediction instrument.

Comparative assessment of the Willems dental age estimation methods: a Chinese population-based radiographic study.

BACKGROUND: The comparison of the two Willems dental age estimation methods (gender-specific (Willems I) and non-gender-specific (Willems II)) has not been fully investigated. Here we aimed to explore the applicability of the Willems dental age estimation in an Eastern Chinese population, which may cast light on the field of dental age estimation. METHODS: A total of 1211 oral panoramic radiographs (582 boys and 629 girls) of the Chinese Han population aged 11-16 years old were collected. Dental ages (DAs) were calculated using the Willems method. Statistical significance was set at a p-value < 0.05. Age differences between chronological age (CA) and dental age were analyzed by paired t-tests and mean absolute error (MAE). RESULTS: The differences between CA and DA determined by the Willems I method were + 0.44 and + 0.09 years for boys and girls, respectively. When using the Willems II method, these differences were + 0.57 and - 0.09. The MAEs of the Willems I method between DA and CA were 0.95 and 1.00 years in boys and girls, respectively. For Willems II, MAEs were 1.02 and 1.00 years in boys and girls. CONCLUSIONS: This study showed that the Willems I method was more accurate than the Willems II method in the boys' group for predicting age from a whole scale. In comparison, Willems II is more competitive in the girls' group. Neither method may be satisfactory for 11-to-16-year-old teenagers in Eastern China.