Background: Recently, the dental age estimation method developed by Cameriere has been widely recognized and accepted. Although machine learning (ML) methods can improve the accuracy of dental age estimation, no machine learning research exists on the use of the Cameriere dental age estimation method, making this research innovative and meaningful.Aim: The purpose of this research is to use 7 lower left permanent teeth and three models [random forest (RF), support vector machine (SVM), and linear regression (LR)] based on the Cameriere method to predict children's dental age, and compare with the Cameriere age estimation.Subjects and methods: This was a retrospective study that collected and analyzed orthopantomograms of 748 children (356 females and 392 males) aged 5–13 years. Data were randomly divided into training and test datasets in an 80–20% proportion for the ML algorithms. The procedure, starting with randomly creating new training and test datasets, was repeated 20 times. 7 permanent developing teeth on the left mandible (except wisdom teeth) were recorded using the Cameriere method. Then, the traditional Cameriere formula and three models (RF, SVM, and LR) were used to estimate the dental age. The age prediction accuracy was measured by five indicators: the coefficient of determination (R²), mean error (ME), root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE).Results: The research showed that the ML models have better accuracy than the traditional Cameriere formula. The ME, MAE, MSE, and RMSE values of the SVM model (0.004, 0.489, 0.392, and 0.625, respectively) and the RF model (− 0.004, 0.495, 0.389, and 0.623, respectively) were lower with the highest accuracy. In contrast, the ME, MAE, MSE and RMSE of the European Cameriere formula were 0.592, 0.846, 0.755, and 0.869, respectively, and those of the Chinese Cameriere formula were 0.748, 0.812, 0.890 and 0.943, respectively.Conclusions: Compared to the Cameriere formula, ML methods based on the Cameriere’s maturation stages were more accurate in estimating dental age. These results support the use of ML algorithms instead of the traditional Cameriere formula.

Previous studies have found that circular RNA (circRNA) hsa_circ_0026827 plays a role during osteoblast differentiation, but the mechanism is unclear. The aim of this study was to illuminate the role of hsa_circ_0026827 in human dental pulp stem cells (DPSCs) during osteoblast differentiation. The results show that hsa_circ_0026827 expression significantly increased during osteoblast differentiation, while knockdown of hsa_circ_0026827 suppressed DPSC-derived osteoblast differentiation. microRNA (miRNA) expression profile analysis showed that downregulation of hsa_circ_0026827 promoted miR-188-3p expression. miR-188-3p downregulation restored osteogenic differentiation of DPSCs after hsa_circ_0026827 was silenced. Luciferase reporter assays verified that miR-188-3p was the target of hsa_circ_0026827 and also demonstrated that Beclin1 and RUNX1 were miR-188-3p downstream targets. miR-188-3p overexpression suppressed DPSC osteogenic differentiation by targeting Beclin-1-mediated autophagy and runt-related transcription factor 1 (RUNX1). In vivo studies using a heterotopic bone model also found that hsa_circ_0026827 overexpression plays an important role in promoting heterotopic bone formation. In conclusion, our research indicates that hsa_circ_0026827 promotes osteoblast differentiation of DPSCs via Beclin1 and the RUNX1 signaling pathways by sponging miR-188-3p, which suggests novel therapeutics for osteoporosis treatment.

Circular RNAs (circRNAs) have been found to be a crucial role in stem cell-associated bone regeneration. However, the functions and underlying mechanisms of circRNAs in the osteogenic differentiation of human dental pulp stem cells (hDPSCs) remain largely unclear. We found that overexpression of circRNA124534 unexpectedly promoted DPSCs osteogenesis in vitro and in vivo. Our results confirmed circRNA124534, acting as a miRNA sponge, directly interacts with miR-496 and consequently regulates β-catenin, which in turn exerts osteogenesis of DPSCs. Enforced expression of miR-496 reversed the osteogenesis of circRNA124534, and suppression of miR-496 enhanced the osteogenic differentiation of DPSCs by promoting β-catenin. In conclusion, our findings demonstrate functions of circRNA124534 in modulating osteogenic differentiation through the miR-496/β-catenin pathway; thus, providing a novel potential target for therapy.

Background: Dental age (DA) estimation using two convolutional neural networks (CNNs), VGG16 and ResNet101, remains unexplored. In this study, we aimed to investigate the possibility of using artificial intelligence-based methods in an eastern Chinese population. Methods: A total of 9586 orthopantomograms (OPGs) (4054 boys and 5532 girls) of the Chinese Han population aged from 6 to 20 years were collected. DAs were automatically calculated using the two CNN model strategies. Accuracy, recall, precision, and F1 score of the models were used to evaluate VGG16 and ResNet101 for age estimation. An age threshold was also employed to evaluate the two CNN models. Results: The VGG16 network outperformed the ResNet101 network in terms of prediction performance. However, the model effect of VGG16 was less favorable than that in other age ranges in the 15–17 age group. The VGG16 network model prediction results for the younger age groups were acceptable. In the 6-to 8-year-old group, the accuracy of the VGG16 model can reach up to 93.63%, which was higher than the 88.73% accuracy of the ResNet101 network. The age threshold also implies that VGG16 has a smaller age-difference error. Conclusions: This study demonstrated that VGG16 performed better when dealing with DA estimation via OPGs than the ResNet101 network on a wholescale. CNNs such as VGG16 hold great promise for future use in clinical practice and forensic sciences.