The teeth are the most challenging material to work with in the human body. Existing methods for detecting teeth problems are characterised by low efficiency, the complexity of the experiential operation, and a higher level of user intervention. Older oral disease detection approaches were manual, time-consuming, and required a dentist to examine and evaluate the disease. To address these concerns, we propose a novel approach for detecting and classifying the four most common teeth problems: cavities, root canals, dental crowns, and broken-down root canals, based on the deep learning model. In this study, we apply the YOLOv3 deep learning model to develop an automated tool capable of diagnosing and classifying dental abnormalities, such as dental panoramic X-ray images (OPG). Due to the lack of dental disease datasets, we created the Dental X-rays dataset to detect and classify these diseases. The size of datasets used after augmentation was 1200 images. The dataset comprises dental panoramic images with dental disorders such as cavities, root canals, BDR, dental crowns, and so on. The dataset was divided into 70% training and 30% testing images. The trained model YOLOv3 was evaluated on test images after training. The experiments demonstrated that the proposed model achieved 99.33% accuracy and performed better than the existing state-of-the-art models in terms of accuracy and universality if we used our datasets on other models.

This longitudinal study aimed to compare the association between gingival phenotype (thin vs. thick) and periodontal disease severity in patients undergoing fixed orthodontic therapy (FOT) and Invisalign treatment over a six-month follow-up period. Clinical periodontal parameters, including full mouth plaque score (FMPS), full mouth bleeding score (FMBS), gingival index (GI), probing pocket depth (PPD), clinical attachment loss (CAL), gingival recession (GR), keratinized tissue width (KTW), transgingival probing, and gingival biotype assessment, were recorded at baseline and 6 months into treatment for both orthodontic groups and a control group. Statistical analysis evaluated differences in parameters between groups and across time points. In the thick phenotype, both Invisalign and FOT groups showed a significant mean reduction in FMPS (baseline to 6 months) by −24.8707 and −12.3489, respectively (p < 0.05). The gingival index decreased significantly for both groups, with Invisalign and FOT showing reductions of −0.83355 and −1.10409, respectively (p < 0.05). FMBS (baseline to 6 months) decreased significantly for Invisalign and FOT, with mean differences of −9.10298 and −12.6579 (p < 0.05). Probing pocket depth (baseline to 6 months) was also significantly reduced for both Invisalign and FOT groups while CAL showed non-significant differences in both groups (p > 0.05). Similar changes were seen in the thin phenotype too. This study highlights the positive influence of both Invisalign and fixed orthodontic therapy on periodontal health, particularly in patients with thin and thick gingival biotypes. These findings, with significant reductions in key periodontal parameters, offer valuable insights to guide orthodontic treatment decisions and enhance patient outcomes.

Objective: The relevance of growth determination in orthodontics is driving the search for the most precise and least invasive way of tracking the pubertal growth spurt. Our aim was to explore whether minimally invasive salivary estimation of biomarkers Insulin-like growth factor (IGF-1) and Insulin-like growth factor binding protein-3 (IGFBP-3) could be used to estimate skeletal maturity for clinical convenience, especially in children and adolescent age groups. Materials and Method: The cross-sectional study was conducted on 90 participants (56 girls and 34 males) with ages ranging from 6 to 25 years. Each subject’s hand-wrist radiograph was categorized based on skeletal maturity, and saliva samples were estimated for IGF-1 and IGFBP-3 using the respective ELISA kits. Kruskal–Wallis nonparametric ANOVA was applied to compare different skeletal stages. Results: The study demonstrated low salivary IGF-1 levels at the prepubertal stage, with increase during pubertal onset and peak pubertal stage followed by a decline during pubertal deceleration to growth completion. Spearman’s correlation coefficient demonstrated a strong positive association (r = 0.98 p < 0.01) between salivary IGF/IGFBP-3 ratio and different stages of skeletal maturity. Conclusion: Salivary IGF-1, IGFBP-3, and IGF/IGFBP-3 ratio could serve as a potential biochemical marker for predicting the completion of skeletal maturity.

Introduction: Green-synthesized nanoparticles show promise as anti-biofilm and antibacterial agents in medical applications, including dental implants and oral devices. However, conventional antibacterial testing methods are laborious and lack sensitivity. Quartz tuning fork (QTF)-based biosensors offer a compelling alternative due to their high sensitivity, compact size, and cost-effectiveness. This study evaluates a QTF biosensor for quantifying the antibacterial activity of green-synthesized ZnO nanoparticles against negative and positive Gram bacteria. Methods: The antibacterial activity of ZnO nanoparticles was tested in a simulated oral environment against Staphylococcus aureus (gram-positive) and Escherichia coli (gram-negative) using a QTF biosensor. Changes in resonance frequency and quality factor were measured to assess bacterial growth inhibition. Experiments were conducted with varying ZnO concentrations (eg, 1 mm) to correlate sensor responses with antibacterial effects. Results: The QTF biosensor detected significant antibacterial activity as resonance frequency decreased by 5.69 ± 3.81 hz (S. aureus) and 30.57 ± 4.01 hz (E. coli) in 1 mm ZnO. Quality factor declined by 31.75 ± 7.55 for E. coli but remained stable for S. aureus. Higher bacterial concentrations (lower ZnO doses) increased damping effects, reducing the quality factor. S. aureus exhibited greater sensitivity to ZnO nanoparticles than E. coli. Discussion: The QTF biosensor successfully quantified the antibacterial effects of green-synthesized ZnO nanoparticles, demonstrating its potential as a rapid, sensitive alternative to traditional methods. The differential responses of S. aureus and E. coli suggest species-specific interactions with ZnO, warranting further study. This approach could streamline the development of biocompatible, antibacterial medical materials.

Key Clinical Message: With conventional mechanics to protract the upper posterior teeth for correcting Class III molar relationships, such as intra‐arch mechanics, face mask reverse‐pull headgear protraction, and interarch Class III elastics, there are some adverse effects, including diminished patient compliance, the possibility of losing anchorage, and extrusion of upper molars and lower incisors with counterclockwise rotation of the occlusal plane. Protraction force should be directed through the center of resistance of the upper posterior teeth to prevent these side effects.

Orthodontics is a specialty derived from dental sciences that starts by diagnosing, preventing, intercepting, modifying growth, and treating all dentofacial discrepancies. Medical interventions can cause harm besides their benefits. This review is done to evaluate the most common potential risks that can be caused by orthodontic treatment and detect the most common factor in the treatment that may lead to this hazard. The review was conducted based on PRISMA-S guidelines for literature search strategy for systematic reviews. Despite all the beneficial effects of orthodontic treatment, it also has negative effects on oral and peri-oral structures and social well-being. It is also important to obtain informed consent from patients before the initiation of any orthodontic procedure, especially in cases with a history of trauma or known adverse reactions.