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.

Objectives: The purpose of the 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 an 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 as primary dentition (n: 1857), mixed dentition (n: 1406), and permanent dentition (n: 2812). U-Net model implemented with PyTorch library was used for segmentation of caries lesions. Confusion matrix was used to evaluation of 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 as 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 as 0.8271, 0.9125, and 0.8677, respectively. In the total group including primary, mixed, and permanent dentition, the sensitivity, precision and F1 score calculated using the confusion matrix were 0.8269, 0.9123 and 0.8675, respectively. Conclusion: Deep learning-based AI models are promising tools for the detection and diagnosis of caries in panoramic radiographs taken from children in different dentitions.

The sweet taste and bitter taste genes are thought to have an influence on obesity and caries, which are chronic diseases. The aim of the study was to investigate the effects of the polymorphisms of TAS2R38 (the bitter taste gene) and TAS1R2 (the sweet taste gene), which are the most important members of the taste gene family, on the dental status of obese and normal-weight children. The study included 78 healthy children and 100 children diagnosed with obesity (5-16 years old). The anthropometric measurements and dental status of the children were evaluated. The decayed, missing and filled permanent/primary teeth (DMFT/dmft) index was determined using the standard methods recommended by the World Health Organization (WHO). Blood samples were collected from all subjects and were analyzed via the polymerase chain reaction (PCR) test, with the use of specific primers for the genetic analysis. Five single-nucleotide polymorphisms (SNPs) of the TAS2R38 and TAS1R2 genes were investigated. The truncated Poisson and truncated negative binomial modeling approaches were used with regard to the data. The DMFT/dmft scores were low in obese children and high in children who did not sense the bitter taste (non-tasters). While obese non-taster children had increased DMFT/dmft scores, normalweight non-taster children had decreased DMFT/dmft scores. The alanine, valine and isoleucine (AVI) as well as proline, alanine and valine (PAV) haplotypes of the TAS2R38 gene are associated with the DMFT/dmft index and obesity. This study showed that the DMFT/dmft scores were decreased in obese children. According to the haplotype analysis of the TAS2R38 gene, the DMFT/dmft scores were increased in non-tasters. When differentiating obese nontasters and control non-tasters, DMFT/dmft increased in obese non-taster patients, while it decreased in control non-taster patients.

Objective: Chemotherapy and radiotherapy, used to treat childhood hematological malignancies (acute lymphocytic leukemia [ALL] and acute myelocytic leukemia [AML]) can negatively affect oral tissues and organs. This study aimed to evaluate the oral health‐related quality of life in children with ALL/AML.Materials and Methods: A total of 99 children, including 49 undergoing ALL/AML (41 with ALL and eight with AML) and 50 healthy volunteers, were included in this cross‐sectional study. The mean age of the entire study group was 78.63 ± 34.41 months. The mean age of the ALL/AML and control groups was 87.12 ± 35.04 and 70.95 ± 34.85 months, respectively. The Simplified Oral Hygiene Index (SOHI), the Decayed, Missing, and Filled Teeth (DMFT/dmft) index, and the Turkish version of the Early Childhood Oral Health Impact Scale (ECOHIS‐T) were administered to all children. The data were analyzed using SPSS software (version 22.0). The Pearson chi‐square and Fisher's exact tests were used to compare demographic data.Results: The age and gender distributions of the two groups were similar. According to ECOHIS‐T, the children in the ALL/AML group had significantly more loss of function (eating, drinking, sleeping, etc.) than those in the control group.Conclusions: Oral health and self‐care were negatively affected by childhood ALL/AML and its treatment.