Vol. 16 No. S1 (2026): International Dental Research

DOI: https://doi.org/10.5577/idr.2026.vol16.S1

Published: 01.01.2026

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Original Article

Prevalence of radix entomolaris and radix paramolaris in mandibular first and second molars in a Turkish population

Aim: This retrospective study determined the prevalence and distribution of extra roots (radix entomolaris [RE] and radix paramolaris [RP]) in mandibular first and second molars in a southeastern Anatolian Turkish population using cone-beam computed tomography (CBCT). Methodology: A total of 2010 mandibular first and second molars from the archived CBCT images of 662 patients (325 males, 337 females) were evaluated for the associations of RE and RP with sex, side, molar type, and unilateral/bilateral presence. Fisher's exact test was used for categorical comparisons and McNemar's exact test for the paired right-left comparison at patient level; prevalences are reported with Wilson score 95% confidence intervals, and the 24 pairwise post-hoc comparisons were corrected for multiplicity by the Holm procedure. Results: An extra root was detected in 3.7% of the examined teeth (75/2010, 95% CI 3.0-4.7%) and 9.5% of the patients (63/662, 95% CI 7.5-12.0%). Prevalence did not differ significantly by sex (8.9% [29/325] in males versus 10.1% [34/337] in females, p = 0.61), side (McNemar exact p = 0.16), or unilateral/bilateral presence (p = 1.00). Although the overall per-tooth prevalence was similar in first and second molars (3.9% versus 3.6%, p = 0.73), the distribution of root type among the 75 affected teeth differed significantly (Fisher exact p = 0.002). RP was the most frequent type in both first (1.95%, 19/974) and second (3.09%, 32/1036) molars but exceeded RE significantly only in the second molars (RE 4/1036, p < 0.001; first molars RE 14/974, p = 0.49). Among the affected teeth, RP was relatively more common in the second molars (Holm-adjusted p = 0.025), whereas the excess of RE in the first molars did not survive correction for multiple testing (Holm-adjusted p = 0.26). Conclusion: The presence of extra roots in mandibular molars within the Turkish population is a key variation for access cavity preparation in endodontics. The different distributions of these roots between the first and second molars emphasize the need for a thorough pre-treatment radiographic assessment.

Cone-beam computed tomography, root canal anatomy, prevalence, tooth abnormalities, radix entomolaris, radix paramolaris

Ayşenur Çam, Merve Defişet, Merve Yeniçeri Özata, Özkan Adıgüzel
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e260663
Abstract Views: 471 Download: 161 10.5577/intdentres.663
Comparative evaluation of the dimensional compatibility of ISO #30/.06 gutta-percha cones from four different manufacturers using an endodontic gauge

Aim: Gutta-percha is the most commonly used biocompatible obturation material in endodontics for the three-dimensional and hermetic sealing of the root canal system. However, despite the dimensions defined in the ISO 6877 standard, dimensional discrepancies between gutta-percha cones and endodontic files have been reported. The aim of this study was to comparatively evaluate the dimensional variability of ISO #30/.06 gutta-percha cones from four different manufacturers using an endodontic gauge. Methodology: A total of 240 ISO #30/.06 gutta-percha cones (n = 60 per brand) from four manufacturers (ProTaper Ultimate, MetaBiomed, Diadent, and EndoArt) were evaluated. Cone diameters were measured at the D0, D5, D10, and D15 levels using a Woodpecker R1 Plus endodontic gauge. Based on the ISO 6877 nominal dimensions, measurements were categorized as undersized, within the nominal range, or oversized. Statistical analysis was performed using Pearson's chi-square and Fisher's exact tests with Bonferroni correction (p < 0.05). Results: Dimensional discrepancies from the nominal ISO values were observed across all measurement levels, with statistically significant differences among the brands (p < 0.05). ProTaper Ultimate and MetaBiomed showed the highest compatibility with nominal dimensions, whereas EndoArt exhibited a consistently high proportion of undersized cones (57.1–67.2%) across all regions. Diadent showed a notably high proportion of oversized cones at the D15 level (66.7%). Effect sizes for between-brand comparisons were large at every level (Cramer's V = 0.386–0.503). Conclusion: ISO #30/.06 gutta-percha cones from different manufacturers did not show complete dimensional compatibility. These discrepancies may adversely affect apical adaptation, particularly in single-cone obturation techniques, potentially reducing clinical success. The use of an endodontic gauge provides a practical means of objectively verifying cone–canal adaptation in clinical practice, helping to prevent microleakage and obturation errors.

Gutta-percha, endodontic gauge, dimensional compatibility, root canal treatment, dimensional accuracy, ISO 6877, root canal obturation

Kumru Şaz, Hülya Nur Nalbant, Celalettin Topbaş
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e260664
Abstract Views: 443 Download: 147 10.5577/intdentres.664
Evaluating the accuracy of ChatGPT and Gemini models in dental specialty exams: A comparative study of pediatric and restorative dentistry

Aim: The integration of large language models (LLMs) such as ChatGPT and Gemini into healthcare education presents novel opportunities for knowledge dissemination and assessment. Despite their growing prominence, empirical evidence regarding their reliability and domain-specific accuracy in dental specialty examinations remains limited. This study aimed to evaluate and compare the performance of four AI-driven LLMs—ChatGPT-3.5, ChatGPT-4o, Gemini 2.0 Flash, and Gemini 2.0 Advanced— by employing official past questions from the Turkish Dental Specialty Examination (DUS) in Pediatric Dentistry and Restorative Dentistry. Methodology: A total of 178 multiple-choice questions (90 in Restorative Dentistry and 88 in Pediatric Dentistry) were obtained from DUS examinations conducted between 2012 and 2017. All items were presented in Turkish to each model via standardized prompts in isolated sessions to prevent cross-interaction and memory bias. Response accuracy was determined by evaluating the correctness of the chosen answers. Comparative analyses between models, domains, and annual trends were conducted using non-parametric statistical tests, including the Mann–Whitney U and Friedman tests. Results: ChatGPT-4o achieved the highest overall accuracy across both Pediatric (90.88%) and Restorative Dentistry (93.02%), whereas ChatGPT-3.5 exhibited the lowest performance in Pediatric Dentistry (70.71%). Statistically significant inter-model differences were observed in Pediatric Dentistry (p = 0.002), but not in Restorative Dentistry (p = 0.254). Temporal analyses highlighted the consistent performance of ChatGPT-4o, while Gemini 2.0 Flash displayed marked fluctuations in accuracy across certain years and subdomains. Conclusion: Among the evaluated LLMs, ChatGPT-4o demonstrated superior and stable performance in addressing dental specialty exam questions, particularly in content requiring contextual comprehension. These findings underscore the potential of advanced AI tools to enhance dental education and examination readiness. Nonetheless, appropriate model selection should be tailored to the complexity of the task, domain specificity, and pedagogical objectives.

Large language models, dental specialty examination, ChatGPT, Gemini, dental education, artificial intelligence

Ayşenur Altuğ Yıldırım, Menekşe Alim
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e260673
Abstract Views: 572 Download: 287 10.5577/intdentres.673
Accuracy of artificial intelligence responses on antibiotic use in endodontics

Aim: The increasing application of generative artificial intelligence (AI) large language models (LLMs) in various fields, including dentistry, gives rise to questions regarding the reliability of the information they produce. The objective of this study was to analyze the accuracy and consistency of responses provided by three artificial intelligence chatbots when answering questions about antibiotic use in endodontics. Methodology: A total of 24 questions (12 dichotomous, 12 open-ended) were developed based on the American Dental Association (ADA) clinical practice guideline on antibiotic use for the urgent management of pulpal- and periapical-related dental pain and intraoral swelling. The questions were presented to three AI chatbots: ChatGPT 3.5, Google Bard (Gemini), and Microsoft Copilot. The questions were asked three times a day over the course of 10 days. The responses obtained were categorized as correct, incorrect, or insufficient according to the guidelines. The results were statistically analyzed using the chi-square test. Results: A total of 2,160 responses were collected from AI chatbot platforms. The overall accuracy rate for responses regarding antibiotic use for dental pain was 72.5%. A statistically significant difference was observed among the accuracy rates of the platforms (p < 0.05), with ChatGPT achieving 82.4%, Google Gemini 71.3%, and Microsoft Copilot 63.7%. No statistically significant differences were found in the accuracy of responses across the morning, noon, and evening sessions for any of the models (p > 0.05). Conclusion: Although AI chatbots hold promise as supplementary knowledge sources for patient guidance in dentistry, their current responses, particularly in response to open-ended questions, lack the depth and reliability required for antibiotic use. Continued efforts are necessary to optimize these tools to ensure clinical relevance and patient safety in endodontic antibiotic therapy.

Antibiotics, accuracy, artificial intelligence, chatbot, ChatGPT, Gemini, Copilot, LLM, natural language processing

Vahide Hazal Abat, Mert Veznikli
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e260681
Abstract Views: 882 Download: 457 10.5577/intdentres.681

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Vol. 16 No. S1 (2026): International Dental Research

Full Issue

Original Article

Prevalence of radix entomolaris and radix paramolaris in mandibular first and second molars in a Turkish population

Comparative evaluation of the dimensional compatibility of ISO #30/.06 gutta-percha cones from four different manufacturers using an endodontic gauge

Evaluating the accuracy of ChatGPT and Gemini models in dental specialty exams: A comparative study of pediatric and restorative dentistry

Accuracy of artificial intelligence responses on antibiotic use in endodontics