Fractal dimension (FD) analysis is a method used to numerically characterize the complexity of structures or tissues. After sinus lift procedures, the graft material gradually transforms into natural bone over time. This study aimed to evaluate the interpretability of this transformation by assessing changes in FD values. In this retrospective study, data from a total of 36 patients (42 hemimaxillae), including 13 females (17 hemimaxillae) and 23 males (25 hemimaxillae), were analyzed. Cone-beam computed tomography (CBCT) images obtained preoperatively and at six months postoperatively from patients who underwent sinus lift surgery with graft placement in the maxillary molar region were included in the evaluation. The pre- and postoperative images were superimposed using anatomical reference points. Examination areas were determined on cross-sectional slices from the grafted and adjacent non-grafted bone areas (used as control) within the maxillary molar region. FD analysis was performed on these areas. FD values from the grafted and control bone regions at baseline and six months were recorded. Paired and independent sample t-tests were used for statistical analysis, with statistical significance set at p < 0.05. FD values derived from CBCT images showed a statistically significant difference between the initial state of the graft immediately after placement (G0) and its state six months postoperatively (G1) (p < 0.05). The mean FD values were 1.297 for G0 and 1.279 for G1. However, no significant difference was observed between the initial state (B0) and the six-month follow-up state (B1) of the trabecular bone in the posterior region used as a control (p > 0.05). The FD values in the G0 group showed minimal variation across gender and age groups, consistently ranging between 1.295 and 1.299. No significant differences in FD values were identified based on gender or age groups (p > 0.05). The findings of this study suggest that FD analysis may serve as a useful adjunctive tool for evaluating graft osteogenesis.

Dental trauma represents a common emergency condition requiring rapid and accurate guidance to prevent permanent damage. In urgent scenarios where access to dental professionals may be delayed, large language models (LLMs) have the potential to provide patients with timely and relevant information. The aim of this study was to comparatively evaluate the dentist rated performance of five widely used LLMs in answering frequently asked questions related to dental trauma emergencies across three predefined components: accuracy, comprehensiveness, and clinical applicability. Based on the guidelines of the International Association of Dental Traumatology (IADT), the ToothSOS application, and frequently asked patient questions, 27 open-ended questions in Turkish were prepared and divided into five clinical subcategories: avulsion, post-replantation care, luxation, fractures, and other traumas. The questions were posed to the models ChatGPT-4o, Claude 3.5, DeepSeek, Microsoft Copilot, and Gemini 2.0 Flash. Twenty experienced dentists evaluated the responses using a 5-point Likert scale, with the evaluators jointly considering three predefined components: accuracy, comprehensiveness, and clinical applicability. A total of 2,700 individual ratings were analyzed using the Friedman test, Bonferroni-corrected Wilcoxon test, and Intraclass Correlation Coefficient (ICC). A significant difference in overall performance was observed among the models (p < 0.001). ChatGPT-4o achieved the highest mean score (4.63 ± 0.57), whereas Gemini received the lowest score (3.87 ± 0.91). Claude, DeepSeek, and Copilot demonstrated similar, moderate performance, ranging approximately between 4.1 and 4.3. Median values were 5 (IQR 4–5) for ChatGPT-4o and Claude and 4 (IQR 3–5) for the other models, indicating that the responses of ChatGPT-4o and Claude were rated more consistently high. Within each model, no statistically significant differences were observed in mean Likert scores across clinical subcategories (p > 0.05). Although LLMs may not be able to replace professional clinical examination, they may serve as a rapid supportive source of patient-oriented information in dental trauma emergencies where access to a dentist is limited. However, model dependent differences, highlight the need for regular verification before patient facing use. The online version contains supplementary material available at 10.1186/s12903-026-08037-8.

Aim: Many pulp capping materials are commercially available, but the radiopacity levels of these materials are not clearly known. In this study, the radiopacity values of ten different pulp capping materials were evaluated by digital radiography. Material and Methods: Ten different pulp capping materials were prepared and placed in 3 mica plate moulds according to the manufacturer's instructions. In addition, a 1 mm thick molar crown section and an 11-step aluminium penetrometer were prepared for use in this study. After initial setting, the materials were kept at 37 °C in a humid environment. One specimen from each group was radiographed with an aluminium penetrometer and a 1 mm thick molar crown section. The digital radiographic images were transferred to the computer and the radiographic densities of the specimens were determined using the computer programme. After these values were converted to millimetre aluminium (mmAl), one-way ANOVA and post-hoc Tamhane tests were applied as statistical analysis. Results: Neoputty (5.19 mmAl) group showed the highest radiopacity values, while Dycal (1.23 mmAl) group showed the lowest radiopacity value (p < 0.05). While the radiopacity values of Dycal, Calcimol LC, Theracal LC, Therecal PT were lower than enamel (p < 0.05); there was no statistically significant difference with dentin (p > 0.05). Conclusion: Among the pulp capping materials tested, MTA-containing pulp capping materials showed acceptable radiopacity and may allow radiological detection during vital pulp treatment.

Aim: The aim of this study is to classify accessory mandibular canals (AMC) and investigate their association with the dimensions of the mandibular canal (MC) to enhance surgical planning and prevent complications in dental treatments. Methods: Cone-beam computed tomography (CBCT) images were evaluated. AMC’s frequency, length, and diameter, including dental, superior, inferior, forward-open, forward-closed, and retromolar types, were determined. Additionally, the dimensions of the MC, including its length and diameter, were measured, and the relationship between the AMC and MC was statistically analyzed. Descriptive statistics, chi-square test, independent samples t-test (for normally distributed data), Kruskal-Wallis test (for non-normally distributed data), and Tamhane post hoc test were used for statistical analysis of the data. Results: AMC was identified in 82 of the 222 hemimandibles examined (36.9%). It was found that the length and diameter of AMCs varied significantly depending on the AMC type (p = 0.000). The diameter of the dental type AMCs (mean 0.40 ± 0.22 mm) was considerably smaller than that of other AMC types (mean 0.86 ± 0.37 mm). The length and diameter of the MC were measured as 69.20 ± 5.10 mm and 2.96 ± 0.57 mm, respectively. While MC length was not found to influence the presence of AMC (p = 0.785), MC diameter was significantly associated with the occurrence of AMC (p = 0.000). Conclusions: AMC, which is critical for improving surgical planning and reducing the risk of complications in dental procedures, is not uncommon. Their presence should be carefully evaluated, particularly in cases where the MC has a larger diameter.

Background: Video-based digital platforms such as YouTube have become important sources of health information, especially for sensitive populations like pregnant individuals. However, the quality and reliability of such content remain a concern, particularly regarding radiographic procedures during pregnancy. This study aimed to evaluate the content quality and reliability of YouTube videos on “dental radiography during pregnancy” using the DISCERN and Global Quality Scale (GQS) tools and to analyze whether these metrics vary based on the source of content. Methods: A cross-sectional descriptive analysis was conducted on publicly available TurkishlanguageYouTube videos between July 5 and July 7, 2025. Videos were assessed by 2 independentreviewers using the DISCERN (5-item, 25-point scale) and GQS (5-point Likert scale). Videos were categorized into “Poor,” “Moderate,” or “Good” quality based on DISCERN scores. Statistical comparisons were made using one-way ANOVA. Results: Among the 42 analyzed videos, 59% were of moderate quality, 23% of low quality, and 18% of high quality. The overall mean DISCERN score was 15.3 Å} 2.7, and the mean GQS score was 3.4 Å} 0.7. No significant differences in DISCERN or GQS scores were found according to content source (doctor, channel, or hospital) (P > .05). Although videos uploaded by individual healthcare professionals received more likes (99.8 Å} 200.9) and had higher view rates (1717 Å} 1248 views/day), the content quality (DISCERN and GQS) was similar across all sources. Videos in the “Good” quality group had significantly higher GQS scores (4.1 Å} 0.3, P = .001). Conclusion: YouTube content on dental radiography during pregnancy is generally of moderate quality. The involvement of healthcare professionals as speakers in all videos may explain the uniformity in content accuracy. GQS appears to be a sensitive tool for distinguishing presentation quality. Increasing professional visibility and promoting evidence-based content on digital platforms is recommended. Cite this article as: S.zen E, .zbey F. An analysis of Turkish YouTube videos on dental radiography during pregnancy: content and source characteristics. Essent Dent. 2026, 4, 0086, doi:10.5152/EssentDent.2026.25086

Large language models (LLMs) are increasingly used to answer medical questions; however, their performance may vary depending on task characteristics. This study evaluated the performance of multiple versions of two widely used LLM families on oral and maxillofacial radiology (OMFR) questions from the Turkish Dental Specialty Examination (DUS) across three assessment phases and examined the influence of cognitive complexity, model family, evaluation phase, and content domain on response accuracy. A comparative repeated-evaluation design was used. A total of 123 text-based OMFR questions from DUS examinations (2012–2021) were submitted to two widely used LLM families (ChatGPT and DeepSeek) across three evaluation phases (May 2025, August 2025, and February 2026). Questions were categorized by content domain and Bloom cognitive level (low vs. high). Model responses were evaluated using official answer keys, and generalized estimating equations (GEE) were applied to account for repeated measurements. A total of 1230 model responses were analyzed, yielding an overall accuracy of 83.7%. Agreement between repeated runs was substantial to almost perfect (κ = 0.689–0.912). Cognitive complexity emerged as the strongest determinant of performance, with low-level questions significantly more likely to be answered correctly than high-level questions (OR = 6.15, p = 0.003). Content domain was also associated with accuracy (p = 0.028), whereas no statistically significant associations were observed for model family or evaluation phase. LLMs demonstrated high accuracy in answering OMFR examination questions; however, performance was more strongly associated with cognitive complexity than with model family or evaluation phase.