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    Osteogenic effects of metformin and exenatide on bone regeneration in non‑diabetic rats: A Micro‑CT and histological study
    (Wolters Kluwer Medknow Publications, 2025) Ozturk, Hasan; Simsek, Neslihan; Akinci, Levent; Ozgocmen, Meltem; Yigit, Dilek Helvacioglu
    Aim: This study aimed to evaluate the osteogenic effects of systemic metformin and exenatide administration on bone tissue regeneration in an experimental rat model by utilising micro‑computed tomography (micro‑CT) and histological analysis. Materials and Methods: Uniform craniotomy defects measuring 3 mm in diameter and 2 mm depth were performed in the parietal bones of 27 female albino Wistar rats, which were randomly divided into three groups: 1) a group receiving 100 mg/kg/day of oral metformin, 2) a group receiving 3 μg/kg/day of intraperitoneal exenatide, and 3) a control group receiving no medication. Bone volume and density at the defect site were evaluated using micro‑CT scanning and analysis. Results: Bone regeneration and the integration of newly formed bone into intact bone were assessed through histological and immunohistochemical examinations. In all three groups, the results showed no significant differences in bone volume, bone density, the presence of fibrous connective tissue, or the complete integration of the defect area into the bone tissue. However, the experimental groups exhibited significant differences in the number of osteoblasts (P = 0.007) and osteoclasts (P = 0.007) when compared to the control group. Conclusions: Metformin and exenatide enhance the activity of osteoblasts and osteoclasts in bone defects, promoting osteogenic potential during the healing process in non‑diabetic rats. © 2025 Journal of Oral and Maxillofacial Pathology.
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    Second mesiobuccal canal segmentation with YOLOv5 architecture using cone beam computed tomography images
    (Springer, 2024) Duman, Suayip Burak; Ozen, Duygu Celik; Bayrakdar, Ibrahim Sevki; Baydar, Oguzhan; Abu Alhaija, Elham S.; Yigit, Dilek Helvacioglu; Celik, Oezer
    The objective of this study is to use a deep-learning model based on CNN architecture to detect the second mesiobuccal (MB2) canals, which are seen as a variation in maxillary molars root canals. In the current study, 922 axial sections from 153 patients' cone beam computed tomography (CBCT) images were used. The segmentation method was employed to identify the MB2 canals in maxillary molars that had not previously had endodontic treatment. Labeled images were divided into training (80%), validation (10%) and testing (10%) groups. The artificial intelligence (AI) model was trained using the You Only Look Once v5 (YOLOv5x) architecture with 500 epochs and a learning rate of 0.01. Confusion matrix and receiver-operating characteristic (ROC) analysis were used in the statistical evaluation of the results. The sensitivity of the MB2 canal segmentation model was 0.92, the precision was 0.83, and the F1 score value was 0.87. The area under the curve (AUC) in the ROC graph of the model was 0.84. The mAP value at 0.5 inter-over union (IoU) was found as 0.88. The deep-learning algorithm used showed a high success in the detection of the MB2 canal. The success of the endodontic treatment can be increased and clinicians' time can be preserved using the newly created artificial intelligence-based models to identify variations in root canal anatomy before the treatment.