Banu Çiçek Tez Yaşar

Aims: To comparatively evaluate adherence to the 2020 International Association of Dental Traumatology (IADT) guidelines and short-term temporal consistency across three large language models (LLMs)-ChatGPT (GPT-5.5), Gemini 2.5 Flash, and Microsoft Copilot-when answering open-ended questions on tooth avulsion management.Methods: Fourteen open-ended questions were categorized into four domains: Emergency and first aid, clinical management, prognosis and critical factors, and advanced decision-making. Each question was submitted to each LLM three times daily for three consecutive days. Two pediatric dentists classified responses as correct or incorrect by consensus using a guideline-based answer key. Cochran’s Q and post hoc McNemar tests were used for comparisons.Results: Among 378 responses, ChatGPT generated the highest number of correct responses (94/126; 74.6%), followed by Gemini (87/126; 69.0%) and Copilot (79/126; 62.7%). The overall intermodel difference was significant (p=0.021), with a significant pairwise difference only between ChatGPT and Copilot (p=0.015). No significant differences were observed in emergency and first aid, clinical management, or prognosis and critical factors (p>0.05). Advanced decision-making differed significantly among models (p=0.008), particularly between ChatGPT and Gemini (p=0.016). No significant within-day or across-day variation was detected (p>0.05).Conclusion: The evaluated LLMs showed no statistically significant short-term temporal variation, but their adherence to IADT recommendations varied across models and clinical domains. ChatGPT achieved the highest performance, while the greatest divergence occurred in the predefined Advanced Decision-Making domain, specifically in questions concerning extra-alveolar dry time and delayed replantation. These models should be used only as adjunctive tools, with recommendations verified against guidelines under professional supervision.

Aims: This study evaluated the performance of three multimodal large language models (LLMs) ChatGPT, Gemini, and Claude in Demirjian-based dental age (DA) estimation from panoramic radiographs compared with specialist dentists. Methods: This retrospective cross-sectional study included 135 panoramic radiographs of children aged 6–13 years. Two specialist dentists confirmed inter-examiner reliability (ICC=0.90) on a pilot subset; the pediatric dentist then assessed all radiographs using the Demirjian method. The radiographs were analyzed once per model using standardized prompts in ChatGPT-5.5 Instant, Gemini 3.5 Flash, and Claude Sonnet 4.6; thus, the reported metrics reflect single-session model performance. Demirjian stages were converted into gender-specific maturity scores and DAs. Performance was assessed using bias, mean absolute error (MAE), and intraclass correlation coefficient (ICC). Statistical analyses included paired-samples t-tests, Wilcoxon, Friedman, Mann–Whitney U, and independent-samples t-tests (p0.05). Conclusion: ChatGPT yielded the lowest MAE relative to CA; Gemini showed the highest specialist agreement, which should not be equated with accuracy. Current multimodal LLMs remain insufficiently accurate and consistent to replace specialist dentists and should be considered adjunctive decision-support tools.

ObjectivesOil pulling (OP) is a traditional Ayurvedic oral hygiene practice that has recently gained renewed interest as a complementary approach to oral health. This study aimed to evaluate dental students' knowledge, attitudes, and practices (KAP) regarding OP and to compare awareness between clinical and preclinical students.Material and Methods This cross-sectional study was conducted among 239 dental students at Ankara Medipol University, including 130 clinical (4th & 5th year) and 109 preclinical (3rd year) students. Data was collected using a pre-validated KAP questionnaire adapted from previous studies. Statistical analyses were performed using a software with descriptive statistics and the Monte Carlo Exact Test.ResultsThe participants comprised 178 females and 61 males, with a mean age of 22.38 ± 1.81 years. Overall, 36.4% (n = 87) of the students reported being aware of OP. No statistically significant difference in awareness was found between clinical and preclinical students (p = 0.279). However, clinical students were significantly more likely to agree that dentists should encourage patients to use oil pulling (p = 0.041). Among participants who responded positively to the relevant questionnaire item, 95.4% incorrectly believed that the oil should be swallowed after the procedure.Conclusion Dental students demonstrated limited awareness and practice of OP, together with important misconceptions regarding its correct application. Incorporating evidence-based information on complementary oral hygiene methods into dental curricula may improve students' knowledge and clinical perspectives.

Background/Objectives: Early childhood caries (ECC) is a chronic inflammatory condition that may impose a systemic burden in pediatric patients. This study aimed to evaluate the association between dental caries severity, classified by dmft (decayed, missing, and filled teeth for primary dentition) scores, and preoperative systemic inflammatory markers derived from routine complete blood counts (CBC). Methods: This retrospective study included 159 children aged 36–71 months. Participants were categorized into three groups based on dmft scores: low (0–3), medium (4–8), and high (≥9). Hematological parameters and inflammatory indices, including neutrophil-to-lymphocyte ratio (NLR) and systemic immune-inflammation index (SII), were analyzed using one-way ANOVA with post hoc comparisons. Results: Significant differences were observed among dmft groups for neutrophil and lymphocyte percentages (p = 0.026 and p = 0.027) and lymphocyte count (p = 0.020). The medium severity group demonstrated higher neutrophil levels and lower lymphocyte values compared to the high severity group (p < 0.05). Although overall group differences for NLR and SII were not statistically significant (p > 0.05), both markers were significantly higher in the medium group than in the high group (p < 0.05). No significant differences were found in hemoglobin, RDW, or platelet parameters. Conclusions: A non-linear trend was observed, with relatively elevated inflammatory markers in the moderate dmft group. These findings suggest that systemic inflammation in ECC is more closely related to disease characteristics than to caries burden alone. CBC-derived parameters may provide supportive but limited value for assessing systemic inflammatory status in pediatric dental patients.

Background/Objectives: Dental plaque is a significant contributor to various prevalent oral health conditions, including caries, gingivitis, and periodontitis. Consequently, its detection and management are of paramount importance for maintaining oral health. Manual plaque assessment is time-consuming, error-prone, and particularly challenging in uncooperative pediatric patients. These limitations have encouraged researchers to seek faster, more reliable methods. Accordingly, this study aims to develop a deep learning model for detecting and segmenting plaque in young permanent teeth and to evaluate its diagnostic precision. Methods: The dataset comprises 506 dental images from 31 patients aged between 8 and 13 years. Six state-of-the-art models were trained and evaluated using this dataset. The U-Net Transformer model, which yielded the best performance, was further compared against three experienced pediatric dentists for clinical feasibility using 35 randomly selected images from the test set. The clinical trial was registered on under the ID NCT06603233 (1 June 2023). Results: The Intersection over Union (IoU) score of the U-Net Transformer on the test set was measured as 0.7845, and the p-values obtained from the three t-tests conducted for comparison with dentists were found to be below 0.05. Compared with three experienced pediatric dentists, the deep learning model exhibited clinically superior performance in the detection and segmentation of dental plaque in young permanent teeth. Conclusions: This finding highlights the potential of AI-driven technologies in enhancing the accuracy and reliability of dental plaque detection and segmentation in pediatric dentistry.