Objective: The aim of this study was to develop convolutional neural network (CNN)-based systems to diagnose calculus, plaque, gingival hyperplasia and gingival inflammation in intraoral images from orthodontic patients. Materials and methods: A dataset of 1,000 lateral and frontal intraoral images from orthodontic patients was used to develop CNN-based models. Periodontology specialists annotated areas of dental calculus, plaque, gingival inflammation, and gingival hyperplasia on the teeth and gingiva. The dataset was divided into training (80%), validation (10%), and test (10%) sets for model development. The YOLOv8 and hybrid U-Net + ResNet50 models were examined. Their performance was evaluated on the basis of accuracy, precision, recall, F ₁ score, Tversky loss, intersection over union, mean average precision, Dice coefficient, and Cohen’s kappa. Results: The mean classification accuracy was 0.96 for the YOLOv8 model and 0.93 for the U-Net + ResNet50 model. On the basis of the Dice coefficient, the models performed best in detecting gingival hyperplasia (YOLOv8: 0.78, U-Net + ResNet50: 0.79) and worst in detecting dental calculus (YOLOv8:0.48, U-Net + ResNet50:0.53). Cohen’s kappa coefficient was highest for classifying gingival hyperplasia (YOLOv8: 0.785, U-Net + ResNet50: 0.790). The precision exceeded 0.72 across all the classifications, with the greatest precision in classifying gingival inflammation. Conclusion: Deep learning-based systems can serve as decision support tools by offering rapid and objective evaluations of dental calculus, plaque, gingival inflammation, and gingival hyperplasia. Nonetheless, the definitive diagnostic conclusion should be based on the clinician’s specialized expertise and professional judgment. Clinical relevance: The integration of CNN-based diagnostic models into clinical workflows has the potential to facilitate early periodontal diagnosis and improve accessibility to periodontal assessments in orthodontic patients.

Background/Objective: Bitewing radiographs are widely used for evaluating dental caries, restorations, and periodontal status due to their low radiation dose and high image quality. While artificial intelligence–based studies are common for other dental imaging modalities, multi-class diagnostic charting on bitewing radiographs remains limited. This study aimed to simultaneously detect eight periodontal and restorative parameters using a YOLOv8x-seg–based deep learning model and to assess its diagnostic performance. Materials and Methods: A total of 1197 digital bitewing radiographs were retrospectively analyzed and annotated by experts, resulting in 7860 labels across eight conditions. Periodontal conditions included alveolar bone loss, dental calculus, and furcation defects, while restorative and dental conditions comprised caries, cervical marginal gaps, open contacts, overhanging fillings, and secondary caries. The dataset was divided on a patient basis into training (80%), validation (10%), and test (10%) sets. The YOLOv8x-seg model was trained for 800 epochs with extensive data augmentation, and performance was evaluated using precision, recall, and F1-score, along with confusion matrices. Results: The model showed the highest accuracy in the alveolar bone loss class (precision: 0.84, recall: 0.93, F1: 0.88). While moderate performance was achieved for dental calculus (F1: 0.58) and caries (F1: 0.57) detection, lower scores were recorded in low-frequency classes such as cervical marginal gap (F1: 0.23), secondary caries (F1: 0.29), overhanging filling (F1: 0.35), furcation defect (F1: 0.40), and open contact (F1: 0.41). The overall segmentation performance achieved an mAP@0.5 of 0.30 and an mAP@0.5:0.95 of 0.10, indicating an acceptable performance level for segmentation-based multi-class models. Conclusions: The obtained findings demonstrate that the YOLOv8x-seg architecture can detect and segment periodontal conditions with high success and restorative parameters with moderate success in automation processes in bitewing radiographs. Accordingly, the model presents a methodologically feasible framework for the multiple and simultaneous radiographic evaluation of periodontal and restorative findings on bitewing radiographs, with performance varying across classes and lower sensitivity observed in low-frequency conditions.

In this study, the relationships between periodontitis and total oxidant status (TOS), total antioxidant status (TAS), and selenium levels were investigated. A total of 122 participants, including 61 periodontitis patients and 61 periodontally healthy individuals, were included. Serum TOS, TAS, and selenium levels were measured, and the biochemical and clinical parameters were compared. The relationship between selenium levels and periodontitis was assessed through univariate analysis and multivariate logistic regression. Compared with the healthy group, the periodontitis group had significantly higher TOS and significantly lower TAS and selenium levels ( p < 0.001). Logistic regression analysis also revealed a significant correlation between selenium levels and periodontitis ( p < 0.001). Our study demonstrated that periodontitis was related to TOS, TAS, and selenium levels. The present study investigated the relationships of periodontitis with TOS, TAS, and selenium levels. Selenium levels could serve as an important biomarker for periodontal disease, as they are strongly correlated with the TOS value, the TAS value, and clinical parameters. Furthermore, lower selenium levels were observed in periodontitis patients than in healthy individuals.

Background/Objectives: This study aimed to evaluate salivary leptin, adiponectin, and calprotectin levels and to investigate the associations among these biomarkers in periodontally healthy individuals, as well as in patients with gingivitis and periodontitis. Methods: A total of 165 participants were included: 55 periodontally healthy individuals, 55 with gingivitis, and 55 with periodontitis. Unstimulated saliva was collected via passive drool, and salivary leptin, adiponectin, and calprotectin levels were biochemically quantified using enzyme-linked immunosorbent assay. Results: Salivary leptin levels were significantly lower in the periodontally healthy group than those in the gingivitis and periodontitis groups, whereas adiponectin levels were reduced in the periodontitis group than in the periodontally healthy and gingivitis groups (p < 0.05). Salivary calprotectin levels differed significantly among groups, highest in the periodontitis group, followed by the gingivitis and periodontally healthy groups (p < 0.05). Salivary leptin and calprotectin levels demonstrated significant positive correlations with all clinical periodontal parameters, while adiponectin levels were negatively correlated (p < 0.05). Receiver operating characteristic and logistic regression analyses identified salivary leptin, calprotectin, and adiponectin levels as significant biomarkers for distinguishing periodontal health, gingivitis, and periodontitis (p < 0.05). Conclusions: These findings suggest salivary leptin, calprotectin, and adiponectin may serve as biomarkers and potential risk predictors of periodontal disease.

Background/Objectives: Orthodontic tooth movement is a biological process involving coordinated bone resorption and formation in response to mechanical stimulation. The aim of this study was to evaluate the temporal changes in C-terminal telopeptide of type I collagen (CTX), procollagen type I N-terminal propeptide (PINP), and vitamin D-binding protein (VDBP) levels in gingival crevicular fluid (GCF) and saliva during fixed orthodontic treatment, as well as to assess the relationships among these biomarkers. Methods: The study included a total of 27 systemically and periodontally healthy individuals comprising 14 males and 13 females. Clinical periodontal parameters were assessed at three time points: before treatment (T0), at 24–48 h (T1), and on day 40 (T2). GCF and saliva samples were collected at the same time points. Levels of CTX, PINP and VDBP in GCF and saliva were quantified using enzyme-linked immunosorbent assay. The data were analyzed using both parametric and non-parametric statistical tests. Temporal changes across the three time points were evaluated using mixed-effects models, differences between GCF and saliva biomarker levels were assessed using paired tests, and correlations were examined using Spearman correlation analysis. Results: GCF and salivary CTX levels demonstrated a significant increase from T0 to T1, while PINP levels exhibited a substantial rise from T1 to T2 (p < 0.001). Levels of VDBP in both GCF and saliva did not demonstrate significant temporal changes (p > 0.05). Higher VDBP levels in both fluids were found to be negatively associated with increases in CTX and positively associated with increases in PINP (p < 0.05). Furthermore, salivary CTX and VDBP levels exhibited a consistent increase compared to those measured in GCF at all time points (p < 0.05). Conclusions: Fixed orthodontic forces elicit sequential resorptive and formative responses in both GCF and saliva. The potential of VDBP to function as a local modulator is indicated, with the capacity to influence the balance between osteoclastic and osteoblastic activity. The evaluation of these biomarkers in non-invasive biological samples may offer a valuable approach for monitoring bone metabolism throughout orthodontic treatment.

Mesenchymal stem cells (MSCs) are candidates for the treatment of palatal wounds in combination with biomaterials. In this study, we developed a method for the production of a ready-to-use mucoadhesive hydrogel containing MSCs for palatal wounds and evaluated its healing effects. Dental follicle MSCs (DFMSCs) were isolated from the dental follicle tissue of a healthy twenty-year-old donor. DFMSCs were suspended in a cell-preserving solution containing platelet-rich plasma, trehalose, and DMSO, and loaded into a catechol–chitosan hydrogel solution at a ratio of 1:400 (v/v) with 5 × 10⁵ or 6 × 10⁶ cells per hydrogel to create a novel lyophilization method for cell integration into the biomaterial. Hydrogels were fabricated as scaffolds with a diameter of 5 mm and a depth of 4 mm. After lyophilization of the hydrogels with cells, a viability test was performed after the production of hydrogels on the seventh day and the fifth month. Palatal wounds were created as full-thickness wounds in rats using a 5 mm diameter punch. The hydrogels were applied to the palatal wounds of rats, and histochemical and immunohistochemical analyses were performed. The results showed that, after rehydration of the hydrogels, DFMSCs had over 80% viability and were homogeneously distributed in the hydrogels. After the application of DFMSC-loaded hydrogels, palatal wounds healed within 7 days, and inflammatory cell infiltration, fibroblastic proliferation, and ulceration were significantly reduced, while epithelial regeneration was significantly increased compared to the control group. The viability ratio of DFMSCs was 83.7% on the seventh day and 71.3% in the fifth month. Hydrogels loaded with DFMSCs represent a promising, ready-to-use biomaterial approach for supporting palatal wound healing.