Dental health is integral to overall well-being, with early detection of issues critical for prevention. This research work focuses on utilizing artificial intelligence and deep learning-based object detection techniques for automated detection of common dental issues in orthopantomography x-ray images, including broken roots, periodontally compromised teeth, and the Kennedy classification of partially edentulous arches. An orthopantomography dataset has been used to train several models employing various object detection architectures, hyperparameters, and training techniques. The performance of these models was evaluated to select the one with the highest accuracy. This selected model was subsequently deployed for further testing and validation on unseen data to assess its real-world performance and potential for clinical application. The proposed model not only facilitates the classification of the Kennedy classification but also offers detailed information about the arch (maxillary or mandibular) and specifies the affected side of the arch (right or left). It can diagnose multiple dental issues simultaneously within an image, enhancing diagnostic capabilities for dental practitioners. Despite a small dataset, satisfactory results were achieved through tailored hyperparameters and a piecewise annotation scheme.

Background and Objectives: Oral health is one of the most significant issues in public health. The Decayed, Missing, and Filled Teeth (DMFT) Index is a useful tool for assessing and measuring the state of oral health in a community. This study aimed to evaluate oral health knowledge, attitudes, and practices among participants who visited a dental clinic at King Faisal University and to evaluate their DMFT scores. Materials and Methods: This cross-sectional, questionnaire-based study was conducted at the King Faisal University dental complex, Kingdom of Saudi Arabia, using a simple random sampling technique. The data were collected using a self-administered structured questionnaire in English and Arabic. All statistical analyses were carried out using the SPSS 20 software. A chi square and ANOVA test were used to assess the association. A p value of <0.05 was considered statistically significant. Results: There were a total of 260 participants, of whom 193 (74.2%) were male and 67 (25.8%) were female. Most participants, 173 (66.5%), were between the ages of 18 and 28. The majority of the participants 191 (73.5%) believed that bad oral hygiene led to gum disease. Additionally, major issues while visiting dental clinics, the importance of routine dental clinic visits, the existence of a connection between oral and general health, brushing time and frequency of change of used brush were significantly influenced by gender (p < 0.05). In terms of the DMFT index, mean numbers of decaying teeth (D) were 4.82 ± 4.15, mean numbers of missing teeth (M) were 1.56 ± 2.94, mean numbers of filled teeth (F) were 5.17 ± 5.28 and mean DMFT score was 11.56 ± 6.32, with a statistically significant difference observed (p < 0.001). Conclusions: This study concluded that, although some of the study participants neglected oral hygiene practices, the majority of participants had good knowledge and attitudes regarding the significance of oral hygiene. Owing to inadequate practices, the decayed, missing, and filled teeth scores increased with increasing age. Additionally, gender had no significant impact on the mean scores for decayed, missing, and filled teeth, although there were significant differences between age groups.

To overcome the scarcity of annotated dental X-ray datasets, this study presents a novel pipeline for generating high-resolution synthetic orthopantomography (OPG) images using customized generative adversarial networks (GANs). A total of 4777 real OPG images were collected from clinical centres in Pakistan, Thailand, and the U.S., covering diverse anatomical features. Twelve GAN models were initially trained, with four top-performing variants selected for further training on both combined and region-specific datasets. Synthetic images were generated at 2048 × 1024 pixels, maintaining fine anatomical detail. The evaluation was conducted using (1) a YOLO-based object detection model trained on real OPGs to assess feature representation via mean average precision, and (2) expert dentist scoring for anatomical and diagnostic realism. All selected models produced realistic synthetic OPGs. The YOLO detector achieved strong performance on these images, indicating accurate structural representation. Expert evaluations confirmed high anatomical plausibility, with models M1 and M3 achieving over 50% of the reference scores assigned to real OPGs. The developed GAN-based pipeline enables the ethical and scalable creation of synthetic OPG images, suitable for augmenting datasets used in artificial intelligence-driven dental diagnostics. This method provides a practical solution to data limitations in dental artificial intelligence, supporting model development in privacy-sensitive or low-resource environments.

In modern dentistry, radiographic imaging is crucial for examining the connection between the maxillary sinus floor and the root apices of the maxillary posterior teeth, particularly when the periapical region is affected by pathology that could result in infectious, inflammatory, or traumatic changes in the maxillary sinus. This study aimed to investigate the prevalence of periapical pathologies in the maxillary posterior teeth and their relationship with maxillary sinus mucosal thickening by using cone-beam computed tomography scans. This retrospective study was conducted on 420 digitized CBCT images which were scanned in sagittal, axial, and coronal views. Out of 420 total images, 223 (53.1%) were of males and 197 (44.9%) were of females. The data were analyzed using SPSS version 28. A total of 2936 posterior maxillary teeth were tested for periapical pathology (PP), 1477 on the right side and 1459 on the left side. In terms of gender, there was no significant relationship between PP in maxillary posterior teeth on both sides and mucosal thickness of the maxillary sinus (p > 0.05). A significant relationship was found between PP in maxillary posterior teeth on both sides and maxillary sinus mucosal thickening (p < 0.05). This study concluded that the prevalence of periapical pathology in the maxillary posterior teeth was significantly associated with a rise in the incidence of maxillary sinus mucosal thickening. Moreover, the primary causative factor for the pathophysiology of the odontogenic maxillary sinus was the periapical pathology in both maxillary first molars.

Objective Natural wingless-related integration site (Wnt) pathway antagonist sclerostin (SOST) has attracted much attention because unusual bone illnesses characterized by the increased bone mass result from its absence of action. The Wnt ligand is prevented from attaching to the Frizzled family receptor when SOST is present. In the active destruction complex, -catenin is phosphorylated. -Catenin molecules do not enter the nucleus and are broken down by a proteasome. As a result, Wnt-responsive genes are not activated, which lowers bone formation and raises bone resorption. A humanized monoclonal antibody called romosozumab binds to and inhibits SOST with significant cardiac side effects. As a result, the current study's objective is to find and screen Food and Drug Administration (FDA) medications that target SOST. Materials and Methods SOST's structure was retrieved from Protein Data Bank (PDB) (ID: 6l6r). Pharmacophore modeling and molecular operating environment-based virtual testing of FDA-approved medicines. Using the Desmond program, docking and molecular dynamics simulations were performed. Results Our findings revealed medications with FDA approval (ZINC000253387843) Amphotericin B. The stability and receptor–ligand interactions are pretty substantial, as demonstrated by the findings of docking and Molecular dynamics simulations, which have a docking score of −7.3 k/mol and root mean square deviation stability at 40 nanoseconds, respectively. Conclusion The suggested pharmacological therapy shows promise since it uses the Wnt pathway to target the primary bone formation mechanism. However, additional prospective studies are required to apply the available data to clinical practice.

Recently, polyetherpolyetherketone (PEEK) has gained popularity as a dental biomaterial. However, there is a lack of consensus on its potential use as an endodontic post-core material. The aim of this review was to systematically critique and synthesize the evidence on PEEK-based post-cores in comparison to other materials. An electronic search was conducted on PubMed, Medline, Embase, ISI Web of Science, Ovid, Cochrane, and ClinicalTrials.Gov using relevant keywords. Seven in vitro studies were included in this review. Meta-analysis of fracture resistance was carried out on results reported in three studies. Overall, in most of the studies, PEEK post-cores performed similar to glass-fiber post-cores. The meta-analysis revealed no significant difference between the fracture strengths of PEEK and glass-fiber post-cores. However, in most studies, several sources of bias were identified. Within the limitations of this review, it may be concluded that mechanical and physical properties of PEEK posts are similar to those of glass-fiber post-cores. Nonetheless, long-term clinical studies are required to translate these conclusions into clinical practice.