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.

Rhythmic incremental growth lines and the presence of melatonin receptors were discovered in tooth enamel, suggesting possible role of circadian rhythm. We therefore hypothesized that circadian rhythm may regulate enamel formation through melatonin receptors. To test this hypothesis, we examined expression of melatonin receptors (MTs) and amelogenin (AMELX), a maker of enamel formation, during tooth germ development in mouse. Using qRT-PCR and immunocytochemistry, we found that mRNA and protein levels of both MTs and AMELX in normal mandibular first molar tooth germs increased gradually after birth, peaked at 3 or 4 day postnatal, and then decreased. Expression of MTs and AMELX by immunocytochemistry was significantly delayed in neonatal mice raised in all-dark or all-light environment as well as the enamel development. Furthermore, development of tooth enamel was also delayed showing significant immature histology in those animals, especially for newborn mice raised in all daylight condition. Interestingly, disruption in circadian rhythm in pregnant mice also resulted in delayed enamel development in their babies. Treatment with melatonin receptor antagonist 4P-PDOT in pregnant mice caused underexpression of MTs and AMELX associated with long-lasting deficiency in baby enamel tissue. Electromicroscopic evidence demonstrated increased necrosis and poor enamel mineralization in ameloblasts. The above results suggest that circadian rhythm is important for normal enamel development at both pre- and postnatal stages. Melatonin receptors were partly responsible for the regulation.