Computer-based technologies play a central role in the dentistry field, as they present many methods for diagnosing and detecting various diseases, such as periodontitis. The current study aimed to develop and evaluate the state-of-the-art object detection and recognition techniques and deep learning algorithms for the automatic detection of periodontal disease in orthodontic patients using intraoral images. In this study, a total of 134 intraoral images were divided into a training dataset (n = 107 [80%]) and a test dataset (n = 27 [20%]). Two Faster Region-based Convolutional Neural Network (R-CNN) models using ResNet-50 Convolutional Neural Network (CNN) were developed. The first model detects the teeth to locate the region of interest (ROI), while the second model detects gingival inflammation. The detection accuracy, precision, recall, and mean average precision (mAP) were calculated to verify the significance of the proposed model. The teeth detection model achieved an accuracy, precision, recall, and mAP of 100 %, 100%, 51.85%, and 100%, respectively. The inflammation detection model achieved an accuracy, precision, recall, and mAP of 77.12%, 88.02%, 41.75%, and 68.19%, respectively. This study proved the viability of deep learning models for the detection and diagnosis of gingivitis in intraoral images. Hence, this highlights its potential usability in the field of dentistry and aiding in reducing the severity of periodontal disease globally through preemptive non-invasive diagnosis.

Objectives: Many countries have enforced lockdowns on their populations due to the coronavirus disease 2019 (COVID‐19) pandemic. This study aimed to assess the effects of the lockdown on dental students.Methods: A longitudinal, repeated cross‐sectional study was conducted to evaluate psychological problems experienced by dental students during the COVID‐19 lockdown in Saudi Arabia. The dental students were selected from different universities using 2‐stage cluster sampling. The validated Arabic version of the 21‐item depression, anxiety, and stress scale questionnaire was distributed at the beginning and end of the lockdown. Mann–Whitney U and Kruskal–Wallis tests were used as appropriate. Chi‐square test was used to compare the proportions between the sociodemographic data, and logistic regression analysis was used to identify variables associated with the students’ responses.Results: A total of 1287 respondents participated in this study (695 first‐survey respondents, 592 second‐survey respondents). There were longitudinally significant differences in the students’ mental health outcomes based on gender, university, class year, and survey time during the COVID‐19 lockdown. The lockdown increased the likelihood of female, single, and junior students experiencing stress. The students who lived alone recorded a high chance of elevated levels of depression, anxiety, and stress, which showed a significant longitudinal reduction during the lockdown. Moreover, the lockdown increased the likelihood of mental health problems among the students staying in households of two persons or two‐five persons.Conclusions: This study indicates the importance of considering the detrimental mental health consequences on dental students in the event of future pandemics.

Predicting dental development in individuals, especially children, is important in evaluating dental maturity and determining the factors that influence the development of teeth and growth of jaws. Dental development can be accelerated in patients with an accelerated skeletal growth rate and can be related to the skeletal growth pattern as a child. The dental age (DA) of an individual is essential to the dentist for planning treatment in relation to maxillofacial growth. A deep-learning-based regression model was developed in this study using panoramic radiograph images to predict DA. The dataset included 529 samples of panoramic radiographs collected from the dental hospital at Imam Abdulrahman Bin Faisal university in Saudi Arabia. Different deep learning methods were applied to implement the model, including Xception, VGG16, DenseNet121, and ResNet50. The results indicated that the Xception model had the best performance, with an error rate of 1.417 for the 6–11 age group. The proposed model can assist the dentist in determining the appropriate treatment for patients based on their DA rather than their chronological age.

Background. The number of patients seeking orthodontic treatment has been consistently increasing. During orthodontic treatment, it is recommended to place the provisional restoration and to delay the final restoration until completion of orthodontic treatment. Recurrent bracket debonding necessitates orthodontists to prepare the bonding area with special measures. Objective. This study aimed to evaluate the effect of different grit sizes of diamond burs and sandblasting surface treatment on the shear bond strength of orthodontic brackets to provisional crowns. Materials and Methods. A total of 75 discs were fabricated from a bisacrylic composite and divided into 5 groups (n = 15) according to surface treatment by black, blue, and green diamond burs and sandblasting in addition to a control group. Metal orthodontic brackets were bonded to discs in a standardized conventional manner. All specimens were subjected to thermocycling with 5000 cycles of alternating 5°C and 55°C waterbaths. The shear bond strength test was performed using a universal testing machine. A scanning electron microscope (SEM) was used to analyze the surface treatment effect and features of debonded surfaces. The amount of composite resin left on the specimen surfaces was analyzed and classified with the adhesive remnant index. One-way ANOVA was performed at α = 0.05. Results. The shear bond strength of specimens treated with sandblasting was significantly higher than that of the control group under thermal aging conditions ( ), as well as blue burs ( ), while no significant differences were found between different grit diamond burs and the controls ( ). Conclusion. Under thermocycling conditions, sandblasting of provisional crowns increases the bond strength of orthodontic brackets.

Background Individuals with psychological distress are at significantly higher risk of arthritis, cardiovascular disease, and chronic obstructive pulmonary disease. However, influence of psychological distress on oral health remains a concern. The purpose of this study was to evaluate psychological distress and its relationship with caries, oral hygiene status, gingival health, and other factors among adults. Methods This cross-sectional study included a sample of 558 adult patients at the Dental Hospital College of Dentistry, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia. The World Health Organization criteria were used to perform an oral examination of participants who responded to a self-administered questionnaire. Psychological distress was evaluated by using the General Health Questionnaire-12 (GHQ-12), which consists of 12 items on a 4-point Likert scale and its score ranges from 0 to 36. The oral health of participants was assessed using the DMFT, plaque, gingival, and simplified oral hygiene indices. Results In this study, 21.5% of participants demonstrated psychological distress, with 16.80% having mild distress and 4.70% having severe distress. The mean score of GHQ-12 of the study sample was 10.47 +/- 5.77. Significantly higher median scores of GHQ-12 were observed among participants over the age of 30 years (p = 0.021), low-income individuals (p = 0.008), smokers (p = 0.019), and those with medical problems (p < 0.001). Participants with psychological distress demonstrated significantly higher caries, poor gingival health, and compromised oral hygiene (p < 0.005). Multiple linear regression models revealed significant relationships between psychological distress and the DMFT index (p < 0.001), plaque index (p < 0.001), gingival index (p < 0.001), and simplified oral hygiene index (p < 0.001) after adjusting for age, sex, nationality, income, and education. Conclusion Psychological distress was prevalent among the participants. Higher age, low-income status, smoking, and medical problems were significantly related to psychological distress. Additionally, psychological distress was significantly correlated with dental caries experience, gingival health, and oral hygiene.

Objective. To assess the prevalence of temporomandibular disorder (TMD) and its association with text anxiety among undergraduate medical, dental, and pharmacy students in Dammam, Saudi Arabia. Material and Methods. This cross-sectional study included health professions students who responded to Fonseca’s questionnaire and Test Anxiety Inventory by Spielberger to evaluate TMD and test anxiety, respectively. TMD score was compared in different categories of students, and bivariate and multiple logistic regression analyses evaluated the influence of test anxiety on TMD. Results. The study included 884 participants (44.8% males and 55.2% females) with a mean age of 21.46 ± 1.36 years. Regarding items of Fonseca’s questionnaire, most students reported being tense/nervous (65.7%) and had headaches (57.5%). About 45.8% of the participants reported no TMD, and remaining had mild (40.4%), moderate (11.3%), and severe (2.5%) TMD. The mean TMD score was significantly higher in students with high test anxiety (25.6 ± 18.32) than those with low test anxiety (20.25 ± 16.97) ( ). Mean test anxiety scores significantly differed among TMD categories ( ) with the lowest score in the no TMD group and the highest in the moderate TMD group. Female gender (adjusted odds ratio 1.4, P 0.039) and high test anxiety (adjusted odds ratio 1.92, ) were significantly associated with increased odds of having TMD. Conclusions. The study revealed a high prevalence of TMD among students. There was a significant association between test anxiety and TMD. The data obtained may guide preventive policies and program on test anxiety and TMD.