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Öğe The association between complex root canal morphology of mandibular anteriors and distolingual roots in mandibular first molars in a Turkish population(Springer Nature, 2025) Kurt, Özge; Solakoğlu, ElifBackground: This study analyzes Cone Beam Computed Tomography (CBCT) images of mandibular anterior teeth (MATs) in the Turkish population to assess canal configurations, anatomical symmetry, and their correlation with distolingual roots (DLRs) in mandibular first molars (MFMs). Methods: In this retrospective study, CBCT images from 2000 patients were analyzed. A total of 12,000 mandibular teeth including six MATs and MFMs from each patient were evaluated using CBCT imaging. Images meeting inclusion criteria were categorized based on Vertucci's root canal morphology system. The data were classified by gender and symmetry patterns. The relationship between MAT root canal complexity and the presence of DLRs in MFMs was assessed. Results: Type I and Type III canal configurations were the most common in MATs. Complex canals appeared more often in females than in males. Among females, a small portion exhibited a unilateral group, while nearly half had a bilateral group. In males, about half showed a bilateral group, with no unilateral cases observed. More than half of MFMs had DLRs. Unilateral DLRs were seen in a notable number of females and a larger number of males. Bilateral DLRs were present in a smaller number of females and a slightly larger number of males. There was a clear link between DLRs and complex MAT canal configurations. Complications in bilateral MATs occurred more frequently in patients with bilateral DLRs. Conclusion: In the Turkish population, DLRs in MFMs are significantly associated with complex MATs canal configurations. CBCT imaging provides essential insights into root canal anatomy, aiding in the early detection of complex morphologies for optimal endodontic treatment.Öğe Evaluating The Readability of Websites Providing Information About Monkeypox(Aksaray Üniversitesi, 2025) Güner, Ece; Yazar, Hümeyra; Türk Bulut, EzgiAim: The aim of this study is to evaluate the readability, reliability, and quality of the content on websites providing information about the monkeypox virus. Material and Method: The Google search engine (www.google.com.tr) was used with the keyword ‘monkeypox,’ and websites from the first 15 pages of search results that were English-language websites, which did not require membership and were freely accessible were included in the study, , from the first 15 pages of search results were included in the study totaling 44 websites. The websites were categorized into four groups: news sites, professional health organizations, government websites, and others. The readability of the texts on the websites was assessed using the Flesch Reading Ease Score (FRES), Gunning Fog Index (GFI), Coleman-Liau Index (CLI), and Simple Measure of Gobbledygook Index (SMOG). The content quality of the texts was evaluated using the JAMA and DISCERN tools.Öğe Segmentation of the nasopalatine canal and detection of canal furcation status with artificial intelligence on cone-beam computed tomography images(Springer, 2025) Deniz, Hatice Ahsen; Bayrakdar, İbrahim Şevki; Nalçacı, Rana; Orhan, KaanObjectives: The nasopalatine canal (NPC) is an anatomical formation with varying morphology. NPC can be visualized using the cone-beam computed tomography (CBCT). Also, CBCT has been used in many studies on artificial intelligence (AI). The “You only look once” (YOLO) is an AI framework that stands out with its speed. This study compared the observer and AI regarding the NPC segmentation and assessment of the NPC furcation status in CBCT images. Methods: In this study, axial sections of 200 CBCT images were used. These images were labeled and evaluated for the absence or presence of the NPC furcation. These images were then divided into three; 160 images were used as the training dataset, 20 as the validation dataset, and 20 as the test dataset. The training was performed by making 800 epochs using the YOLOv5x-seg model. Results: Sensitivity, Precision, F1 score, IoU, mAP, and AUC values were determined for NPC detection, segmentation, and classification of the YOLOv5x-seg model. The values were found to be 0.9680, 0.9953, 0.9815, 0.9636, 0.7930, and 0.8841, respectively, for the group with the absence of the NPC furcation; and 0.9827, 0.9975, 0.9900, 0.9803, 0.9637, and 0.9510, for the group with the presence of the NPC furcation. Conclusions: Our results showed that even when the YOLOv5x-seg model is trained with the NPC furcation and fewer datasets, it achieves sufficient prediction accuracy. The segmentation feature of the YOLOv5 algorithm, which is based on an object detection algorithm, has achieved quite successful results despite its recent development.
