Makale Koleksiyonu
Bu koleksiyon için kalıcı URI
Article Collection
Güncel Gönderiler
Öğe Bibliometric analysis of articles about the use of mta in endodontics(Tıbbi Kayıtlar Derneği, 2025) Kurt, Özge; Koyuncu, Sümeyye; Güleç, MelikeAim: The aim of this study was to perform a bibliometric and visualized analysis to identify and critically appraise articles on the use of Mineral Trioxide Aggregate (MTA) in endodontics. Material and Method: A Web of Science (WoS) search was conducted on November 25, 2024, using the keywords ‘root canal treatment’ AND ‘mineral trioxide aggregate’ OR ‘MTA’ under the ‘All Fields’ option. Only articles of the ‘Article’ type were included in the study. The WoS category was limited to ‘Dentistry Oral Surgery Medicine,’ and the WoS index was filtered using ‘Science Citation Index Expanded,’ resulting in a total of 415 articles published between 1999 and 2024. The data of the studies were downloaded in ‘BibTeX’ format from WoS and analyzed using Biblioshiny software. Results: It was found that 1398 authors contributed to the research in this field, 882 keywords were used, and 45 journal sources were referenced. A total of 415 articles were published with an annual growth rate of 14.09%. The majority of the articles were published in the Journal of Endodontics, with the United States being the leading country in terms of publication volume. The most frequently used keywords by authors in this field included ‘mineral trioxide aggregate.’ Conclusion: The positive annual growth rate indicates an increasing interest in the field. The Journal of Endodontics holds a leading position in this domain. The United States ranks first in both publication and citation counts. This study expands the existing literature and offers new research topics to researchers, serving as an important reference for future studies related to MTA use in endodontics, especially in the national context.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.
