BackgroundDental anxiety is a widespread problem in many populations. This problem can be a barrier to dental care and may lead to poor oral health. Dental anxiety may be related to psychological status.AimsThe aim of the present study was to assess the levels of dental anxiety, dental fear, Beck Depression, and state-trait anxiety according to age, gender and education level in patients at the periodontology clinic in the Diyarbakır Mouth and Dental Health Center.Study DesignA total of 231 patients (115 males, 116 females) filled out dental fear scale (DFS), dental anxiety scale (DAS), Beck Depression Inventory (BDI), state-trait anxiety inventory-state (STAI-S), and state-trait anxiety inventory–trait (STAI-T) questionnaires, and evaluations of DFS, DAS, BDI, STAI-S, and STAI-T were conducted according to age, gender, and education level.ResultsThe mean DFS, DAS, BDI, STAI-T, and STAI –S were 45.64, 9.15, 13.16, 38.90, and 40.18, respectively. There was a significant association among DFS, DAS, BDI, STAI-S, and STAI-T (p < 0.05). These surveys scores were significantly higher in females than males (p < 0.05). The results of this study indicated that gender age and education level have important effects on DFS, DAS, BDI, STAI-S, and STAI-T (p < 0.05).ConclusionDental anxiety and dental fear were found to be related to psychological status (BDI, STAI-S, and STAI-T) over time. There are some patients with unaddressed psychological distress.

When the distribution ratio of tooth color was examined, different ratios were determined based on gender and age and between the maxillary central, lateral incisors, and canine teeth. A uniform tooth color should not be chosen for anterior restorations, and factors such as gender and age should be considered when making a color selection for patients.

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The automated segmentation of dental restorations is a critical step in diagnosing dental problems and suggesting the best treatment. Some restorations may be missed during a dental examination, depending on the number of patients, the dentist's experience, and fatigue. Automatic detection of dental restorations based on deep learning has the potential to provide a quick radiological assessment based on the patient's treatment history and pre-diagnosis. This study presents a deep learning-based method for automatic detection and classification of amalgam and composite fillings on panoramic images. A total of 250 anonymized panoramic images with amalgam and composite fillings with a resolution of 2048 Â 1024 px were used. In this study, U-Net models with various backbones were employed. The ResNext50 model has achieved the highest pixel accuracy and intersection over union (IoU) performance based on the evaluation of various ResNet and ResNext backbones. The mean IoU value obtained by the model on the test images is 0.767 while the Pixel Accuracy of 99.81% was achieved. Our proposed method demonstrated superior performance compared to similarly conducted studies in the literature. The proposed method can potentially be employed in clinical settings to detect dental restorations automatically. The classification and detection of dental restorations with this model can aid dentistry education at higher institutions as an education tool and make the reporting easier for the dentist.

Introduction: Pathogens, such as cytomegalovirus, hepatitis B virus, hepatitis C virus, herpes simplex virus types 1 and 2, and human immunodeficiency virus are transmitted, threatening the health of dental laboratory workers, especially as a result of saliva and blood contact of patients. To prevent cross-infection, impression materials should be disinfected at the end of the impression process in the mouth. Aim: To study the effect of application time of sodium hypochlorite and quaternary ammonium-based disinfectant solution on the surface roughness of an elastomeric impression material. Materials and Methods: In this in-vitro study done during March 2020, 10 disc-shaped samples were used in each group, with a total of 110 samples obtained from a light body elastomeric impression material with dimensions of 15×3 mm. The samples were kept in a sodium hypochlorite solution (Wizard; Rehber Kimya, Istanbul, Turkey) at concentrations of 1% and 5% for 1, 5, 10, and 30 minutes and in a quaternary ammonium-based disinfectant (Zeta 7 Solution, Zhermack SpA, Italy) for 10 and 30 minutes. Surface roughness measurements were taken with a profilometer device. The data obtained were statistically analysed by Kolmogorov-Smirnov test and Wilcoxon signed rank test for dependent/paired groups for the continuous data. The significance level was set to α=0.05. Results: A statistically significant difference was found between the control group and the 1% sodium hypochlorite (30 min p-value 0.037), and 5% sodium hypochlorite (30 min p-value 0.017). The statistical evaluation of the surface roughness of the samples with different concentrations of sodium hypochlorite and the same times was done and found statistically significant at 30 mins, p-value 0.021. Conclusion: The prolonged application of the sodium hypochlorite disinfectant at 1% and 5% concentrations caused a significant increase in the light body elastomeric impression material’s surface roughness