Background: Dental caries is a prevalent, complex, chronic illness that is avoidable. Better dental health outcomes are achieved as a result of accurate and early caries risk prediction in children, which also helps to avoid additional expenses and repercussions. In recent years, artificial intelligence (AI) has been employed in the medical field to aid in the diagnosis and treatment of medical diseases. This technology is a critical tool for the early prediction of the risk of developing caries. Aim: Through the development of computational models and the use of machine learning classification techniques, we investigated the potential for dental caries factors and lifestyle among children under the age of five. Design: A total of 780 parents and their children under the age of five made up the sample. To build a classification model with high accuracy to predict caries risk in 0–5-year-old children, ten different machine learning modelling techniques (DT, XGBoost, KNN, LR, MLP, RF, SVM (linear, rbf, poly, sigmoid)) and two assessment methods (Leave-One-Out and K-fold) were utilised. The best classification model for caries risk prediction was chosen by analysing each classification model’s accuracy, specificity, and sensitivity. Results: Machine learning helped with the creation of computer algorithms that could take a variety of parameters into account, as well as the identification of risk factors for childhood caries. The performance of the classifier is almost unbiased, making it generalizable. Among all applied machine learning algorithms, Multilayer Perceptron and Random Forest had the best accuracy, with 97.4%. Support Vector Machine with RBF Kernel (with an accuracy of 97.4%) was better than Extreme Gradient Boosting (with 94.9% accuracy). Conclusion: The outcomes of this study show the potential of regular screening of children for caries risk by experts and finding the risk scores of dental caries for any individual. Therefore, in order to avoid dental caries, it is possible to concentrate on each individual by utilizing machine learning modelling.

Aim: The aim of this study was to compare the marginal leakage of hybrid and microfilled composite resin in Class V restorations with and without an enamel bevel. Methods and Materials: Fifty-six cavities were prepared on the buccal and lingual surfaces of 28 extracted human molars using a round bur with the dimensions of 3×2×1.5 mm. The specimens were divided into two groups of 28 based on the cavosurface margin configuration (beveled and non- beveled). Each group was then divided into two subgroups (n=14) based on the type of composite resin (microfilled and hybrid) used for restoration. After completing restorative procedure, specimens were thermocycled and immersed in 0.5% basic fuchsine. Samples were embedded in polyester and then sectioned both mesiodistally and buccolingually. Dye penetration was observed with a stereomicroscope at 25x magnification. Statistical nonparametric analysis Kruskal-Wallis and Mann-Whitney tests were performed to compare the data (a=0.05). Results: There was no statistically significant difference between the two types of composites and two types of enamel margins with respect to microleakage (P>5%). The degree of microleakage at the gingival margin located in dentin of each group was more than that of the enamel margin (P<5%). Conclusion: An enamel bevel in a Class V cavity preparation had no effect on the reduction of marginal leakage using either hybrid or microfilled composite resin. Citation: Bagheri M, Ghavamnasiri M. Effect of Cavosurface Margin Configuration of Class V Cavity Preparations on Microleakage of Composite Resin Restorations. J Contemp Dent Pract 2008 February; (9)2:122-129.