Key words: Silicate cement; Shear strength; Silorane resin

Objective: To determine presence and distribution of enamel defects, recurrent oral aphthous lesions (RAS) and dental caries in children with Celiac Disease (CD) and compare the results with a healthy control group.Methods: Twenty- five CD patients age between 4- 16 years with no other systemic disease, were examined in Pediatric Gastroenterology Clinic of Erciyes University, Faculty of Medicine (Kayseri, Turkey) and then referred to Department of Pediatric Dentistry, Faculty of Dentistry for dental examination and treatment. The control group (25 patients) consisted healthy patients referred to the Department of Pediatric Dentistry, Faculty of Dentistry, Erciyes University for restorative treatment. Both the CD group and control group was examined by the same investigator for the following; (1) enamel defects, (2) recurrent aphthous stomatitis, (3) dental caries.Results: The mean dmft values for the CD group and control group were 3.25

Objectives: The purpose of this study was to evaluate the effectiveness of dental caries segmentation on the panoramic radiographs taken from children in primary dentition, mixed dentition, and permanent dentition with Artificial Intelligence (AI) models developed using the deep learning method. Methods: This study used 6075 panoramic radiographs taken from children aged between 4 and 14 to develop the AI model. The radiographs included in the study were divided into three groups: primary dentition (n: 1857), mixed dentition (n: 1406), and permanent dentition (n: 2812). The U-Net model implemented with PyTorch library was used for the segmentation of caries lesions. A confusion matrix was used to evaluate model performance. Results: In the primary dentition group, the sensitivity, precision, and F1 scores calculated using the confusion matrix were found to be 0.8525, 0.9128, and 0.8816, respectively. In the mixed dentition group, the sensitivity, precision, and F1 scores calculated using the confusion matrix were found to be 0.7377, 0.9192, and 0.8185, respectively. In the permanent dentition group, the sensitivity, precision, and F1 scores calculated using the confusion matrix were found to be 0.8271, 0.9125, and 0.8677, respectively. In the total group including primary, mixed, and permanent dentition, the sensitivity, precision, and F1 scores calculated using the confusion matrix were 0.8269, 0.9123, and 0.8675, respectively. Conclusions: Deep learning-based AI models are promising tools for the detection and diagnosis of caries in panoramic radiographs taken from children with different dentition.

Background and Objective: Dental treatments cannot be always performed under local anesthesia in pediatric non-cooperative patients. For this purpose, different anesthetic techniques have been applied to increase patient comport to dental treatments.Methods: Sixty children classified as ASA I-II, between aged 3 to 9, who were scheduled to undergo tooth extraction, were enrolled for this randomized study. Group K received 1 mg/kg ketamine, Group P received 1 mg/kg propofol, and Group KP received 0.5 mg/kg propofol plus 0.5 mg/kg ketamine intravenously for anesthesia induction.Results: Recovery time was significantly lower in Group P than Group KP. No significant differences were found between groups regarding HR, before and after the induction, at tenth minute. Fifth minute

Objective: The aims of this case–control study were to (a) compare the caries experience and oral hygiene, and (b) quantify the persistence of a delay in the dental age in children with cardiac disease and a group of healthy children.Methods and Materials: The study population comprised a group of 268 3- to 16-year-old children and adolescents with a cardiac disease and a group of 268 age- and sex-matched healthy children and adolescents. Specifically, the decayed, missed, and filled teeth indices, simplified oral hygiene index, and the dental ages of the two groups of children were calculated and then compared.Results: Although the oral health of the children with either a congenital or an acquired heart disease was the same as that of the healthy children, there were significant differences in the decayed, missed, and filled teeth indices. Dental ages of the children with a congenital heart disease were significantly lower than those of healthy children. The findings showed that complex univentricular heart diseases had the highest negative impact on dental development (−1.1), followed by complex biventricular (−0.9), simple surgical (−0.5), and mild (−0.4) heart disease patients.Conclusion: Once thorough knowledge of the child's cardiac status is gained, a definitive dental treatment plan for the child with a cardiac disease can be established.