Teledentistry is a part of telemedicine and can be used for dental diagnosis, dental care, consultation, and dental education. The objective of this study was to assess the awareness, knowledge, and attitudes of undergraduate dental students toward teledentistry, as well as to investigate the influence of enrolling in a teledentistry course on these variables. A structured questionnaire-based online survey (Google Form) was used as the survey tool to evaluate the undergraduate dental students’ awareness, knowledge, and attitudes towards teledentistry. All undergraduate dental students during the 2022–2023 academic year, were invited to join this questionnaire. The questionnaire was posted to the students who agreed to answer the survey online. Associations between categorical variables were evaluated using the Pearson Chi-square test or Fisher-Freeman-Halton Exact test. The Mann-Whitney U test was used to compare two independent groups, and the Kruskal-Wallis test was used to compare more than two independent groups. All analyses were conducted using IBM SPSS Statistics for Windows, Version 23.0 (IBM Corp. Released 2015, Armonk, NY: IBM Corp.). The significance level was set at p < 0.05 for all analyses. Explanatory factor analysis and item analysis were used for the evaluation of the validity of the questionnaire. There were 293 participants from five classes, with a 36.08% response rate; 190 (64.8%) were female and 103 (35.2%) were male, with a median age of 21 years (minimum 17 years, maximum 44 years). In this study, 37.9% of the participants had heard of teledentistry, 30.7% knew what teledentistry is, approximately one-third of the students were aware that there is a course called teledentistry in our faculty, and only 11.6% took the course. Undergraduate dental students had high levels of knowledge and awareness about the advantages and limitations of teledentistry. Dental students who had taken the course had a more positive attitude towards using teledentistry. Enhancing dental students’ knowledge and awareness of teledentistry could beneficially influence their utilization of teledentistry in their future careers. This study is not a clinical trial involving medicinal products or therapeutic interventions, so the clinical trial number is not applicable.

Aims: This study aimed to assess the prevalence of different types of errors in panoramic radiography and explore potential correlations between these errors, age, and gender. Methods: A total of 2000 panoramic radiographs randomly selected from the patient archives of the Department of Oral, Dental, and Maxillofacial Radiology at Dicle University Faculty of Dentistry, taken in 2023 for various indications, were analyzed. Two experienced radiologists (BK and EK) reviewed the digital panoramic X-ray images. Interobserver agreement was assessed by having both observers re-evaluate 10% of the sample. Patients with developmental anomalies, history of trauma, orthognathic surgery, maxillofacial pathology (e.g., tumors/cysts), and those under 16 years old were excluded. Statistical analyses were conducted using SPSS 20.0 software (IBM Corp., Armonk, NY, USA), with a significance level set at α=5%. Cohen’s kappa statistics were utilized to calculate intraobserver agreement. Results: In the analysis of 2000 panoramic radiographs, it was found that 81.16% exhibited at least one error. The predominant error identified was the misplacement of the tongue against the palate, accounting for 34.75% of cases. A statistically significant association was observed between increasing patient age and higher error rates (p0.05). Conclusion: The prevalence of positioning errors in panoramic radiography is considerable. It underscores the importance of providing adequate training to healthcare professionals and technicians to mitigate the risk of misinterpretation and unnecessary exposure to radiation.

Background/Objectives: Current evidence regarding the association between the temporal bone and paranasal sinus pneumatization remains limited. This study aims to investigate the potential morphological association between zygomatic process pneumatization and sphenoid sinus pneumatization using three-dimensional cone-beam computed tomography. Methods: Cone-beam computed tomography images from 573 individuals aged 16 to 87 years (170 males, 403 females) were evaluated in this study. Zygomatic process pneumatization was assessed in two forms: pneumatized glenoid fossa (a radiolucent defect on the glenoid fossa roof) and pneumatized articular eminence (a radiolucent defect within the articular eminence). The sphenoid sinus was classified into four major pneumatization types: conchal, presellar, sellar, and postsellar. The postsellar configuration was additionally divided into four subtypes—subdorsal, dorsal, occipital, and combined—according to its posteroanterior orientation. Lateral sphenoid sinus pneumatization was categorized into pterygoid, greater wing, full lateral (combining pterygoid and greater wing), lesser wing, and anterior types. Results: The analysis revealed a significant relationship between zygomatic process pneumatization and sphenoid sinus pneumatization (p < 0.001), where the former was detected in 64.0% of participants. The postsellar type represented the most frequent form of sphenoid sinus pneumatization (55.5%), whereas the conchal type was the rarest (1.2%). Conclusions: A significant correlation was observed between the zygomatic process and sphenoid sinus pneumatization, with individuals exhibiting the former tending to display more extensive sphenoid sinus pneumatization

Tonsilloliths can be seen on panoramic radiographs (PRs) as deposits located on the middle portion of the ramus of the mandible. Although tonsilloliths are clinically harmless, the high risk of misdiagnosis leads to unnecessary advanced examinations and interventions, thus jeopardizing patient safety and increasing unnecessary resource use in the healthcare system. Therefore, this study aims to meet an important clinical need by providing accurate and rapid diagnostic support. The dataset consisted of a total of 275 PRs, with 125 PRs lacking tonsillolith and 150 PRs having tonsillolith. ResNet and EfficientNet CNN models were assessed during the model selection process. An evaluation was conducted to analyze the learning capacity, intricacy, and compatibility of each model with the problem at hand. The effectiveness of the models was evaluated using accuracy, recall, precision, and F1 score measures following the training phase. Both the ResNet18 and EfficientNetB0 models were able to differentiate between tonsillolith-present and tonsillolith-absent conditions with an average accuracy of 89%. ResNet101 demonstrated underperformance when contrasted with other models. EfficientNetB1 exhibits satisfactory accuracy in both categories. The EfficientNetB0 model exhibits a 93% precision, 87% recall, 90% F1 score, and 89% accuracy. This study indicates that implementing AI-powered deep learning techniques would significantly improve the clinical diagnosis of tonsilloliths.