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    Effect of Different Obturation Techniques on Sealer Penetration in Teeth with Artificial Internal Root Resorption: a Confocal Laser Microscope Analysis
    (Quintessence Publishing Co Inc, 2023) Ugur Aydin, Zeliha; Kara, Irem Cansu; Er Karaoglu, Gamze; Dogan Cankaya, Tulin
    Objective: To investigate the efficacy of different obturation techniques on sealer penetration in teeth with internal root resorption using confocal laser microscopy. Methods: An artificial internal resorption cavity (3 mm deep and 1.2 mm in diameter) was formed in the round-shaped root canals of 45 single-rooted teeth at a distance of 7 mm from the apex, then roots were instrumented (size 40/.06). The samples were divided into three groups (n = 15) according to the obturation technique: lateral compaction (LC), warm vertical compaction (WVC) and carrier-based (CB). Results: In the resorption regions, the sealer penetration depth in the CB and LC groups was significantly higher than that in the WVC group (P < 0.05). Conclusion: Within the limitations of this study, the penetration depth of the sealer in the resorption region was higher in the CB and LC groups as compared to that in the WVC group.
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    The Impact of Language Variability on Artificial Intelligence Performance in Regenerative Endodontics
    (Mdpi, 2025) Buyukozer Ozkan, Hatice; Dogan Cankaya, Tulin; Kolus, Turkay
    Background: Regenerative endodontic procedures (REPs) are promising treatments for immature teeth with necrotic pulp. Artificial intelligence (AI) is increasingly used in dentistry; thus, this study evaluates the reliability of AI-generated information on REPs, comparing four AI models against clinical guidelines. Methods: ChatGPT-4o, Claude 3.5 Sonnet, Grok 2, and Gemini 2.0 Advanced were tested with 20 REP-related questions from the ESE/AAE guidelines and expert consensus. Questions were posed in Turkish and English, with or without prompts. Two specialists assessed 640 AI-generated answers via a four-point rubric. Inter-rater reliability and response accuracy were statistically analyzed. Results: Inter-rater reliability was high (0.85-0.97). ChatGPT-4o showed higher accuracy with English prompts (p < 0.05). Claude was more accurate than Grok in the Turkish (nonprompted) and English (prompted) conditions (p < 0.05). No model reached >= 80% accuracy. Claude (English, prompted) scored highest; Grok-Turkish (nonprompted) scored lowest. Conclusions: The performance of AI models varies significantly across languages. English queries yield higher accuracy. While AI shows potential for REPs information, current models lack sufficient accuracy for clinical reliance. Cautious interpretation and validation against guidelines are essential. Further research is needed to enhance AI performance in specialized dental fields.