Muhammet Can Eren

Background/Objectives: Three-dimensional data that evaluate tooth morphology and dental arch dimensions together across sagittal skeletal patterns are limited. This cone-beam computed tomography (CBCT) study compared tooth morphology and dental arch dimensions among skeletal Class I, II, and III malocclusions and examined their relationships. Methods: This retrospective, cross-sectional study included 111 individuals (37 per group; 24 females and 13 males in each group), selected from archived CBCT records in reverse chronological order with sex-frequency matching. Skeletal class was based on the ANB angle. Crown length, root length, and mesiodistal width of the central incisors, canines, second premolars, and first molars, together with arch perimeter and intercanine, inter-second premolar, and intermolar widths, were measured. Groups were compared using patient-clustered Gaussian generalized estimating equations with Holm-adjusted contrasts and Benjamini–Hochberg false discovery rate (FDR) correction across 35 outcomes; 108 tooth–arch associations were adjusted for class, age, and sex. All analyses were exploratory. Results: Five of 35 outcomes showed nominal class effects (p < 0.05), mainly in the mandible (central incisor and canine crown lengths, arch perimeter, and intercanine width), but none remained significant after FDR correction (q ≥ 0.169). In exploratory contrasts, mandibular arch perimeter was greater in Class III than in Class I (2.20 mm; 95% CI 0.56–3.85). In the primary models, no mesiodistal width or root length differed nominally. Nine of 108 tooth–arch associations remained significant after FDR correction (standardized β 0.27–0.36), six involving premolar and molar mesiodistal widths. Conclusions: No difference in tooth morphology or arch dimensions among skeletal classes remained significant after multiplicity correction; the nominal mandibular findings are exploratory. The findings are descriptive, cannot establish causality, and do not support class-specific treatment recommendations.

The aim of this cone-beam computed tomography (CBCT)-based retrospective study was to evaluate the age-related prevalence of closed apices in permanent maxillary and mandibular second molars in a Turkish pediatric and adolescent population. Additionally, potential sex-related differences in closed apices were investigated. This study included 357 individuals (714 teeth) aged 12-21 years whose CBCT scans were retrieved from the digital archive. The apical status of permanent second molars was evaluated three-dimensionally using multiplanar reconstruction. According to root development, teeth were classified into two groups: those with closed apices in all roots and those with at least one open apex. Age was categorized into seven groups (12-12.99, 13-13.99, 14-14.99, 15-15.99, 16-16.99, 17-17.99, and ≥ 18 years). Associations between closed apices, age, and sex were analyzed using Pearson's chi-square test and Fisher's exact test (p < 0.05). The prevalence of closed apices increased markedly with age in both jaws. Rates were low in the 12-12.99 age group (maxillary 2.1%; mandibular 5.4%), exceeded 60% by 14-14.99 years, and approached completion by 16-16.99 years, reaching 100% in individuals aged ≥ 18 years. Overall, no statistically significant difference was observed between permanent maxillary and mandibular second molars (p > 0.05). However, in the 13-13.99 and 14-14.99 age groups, females demonstrated significantly higher rates of closed apices than males in both jaws; in the 15-15.99 age group, this difference was significant only for permanent maxillary second molars (p < 0.05). Closed apices in permanent second molars show a marked age-related increase and tend to occur earlier in females than in males during certain adolescent age groups. These findings support the use of closed apices in second molars as a reliable indicator for both endodontic treatment planning and forensic age estimation. Knowledge of the relationship between age and sex and the rates of closed apices in permanent second molars play an important role in both forensic dentistry and in endodontic and pediatric treatment planning.

The aim of this study is to evaluate and compare the accuracy of large language models (LLMs) such as ChatGPT-4, DeepSeek-R1, Google Gemini and Microsoft C

Aim: A precise understanding of the morphometric characteristics of the infraorbital foramen (IOF) is essential for ensuring safe and effective surgical interventions and regional anesthesia in children and adolescents with cleft lip and palate (CLP). This study aimed to investigate the morphometric characteristics of the IOF using CBCT in children and adolescents with unilateral cleft lip and palate (UCLP) and to compare the cleft side (CS) with the non-cleft side (NCS). Materials and Method: CBCT scans of 48 individuals with UCLP were analyzed, evaluating a total of 96 IOFs. Reference anatomical landmarks included the supraorbital margin (SOM), infraorbital margin (IOM), nasion (N), anterior nasal spine (ANS), tuber maxilla (TM), sella (S), lateral margin of the apertura piriform (LAP), jugale (J), and midline (M). Distances from the IOF to these landmarks were measured and compared between the CS and NCS. Soft tissue thickness over the IOF was also assessed, and the IOF shape was evaluated separately for each side. Results: The V-oval form was the most common IOF shape on both sides. No significant differences were found in vertical or horizontal diameters between the CS and NCS (p > 0.05). Distances from the IOF to IOM, SOM, S, N, LAP, and midline were significantly shorter on the CS (p < 0.05), whereas distances to ANS and J were significantly longer on the CS (p < 0.05). No significant differences were observed in IOF-TM distances or soft tissue thickness (p > 0.05). Conclusions: In individuals with UCLP, the IOF exhibits significant side-specific variations relative to key anatomical landmarks. These differences should be considered in infraorbital nerve block administration and surgical planning to improve accuracy and safety.

Background/Objectives: The infraorbital canal (IOC) is a critical anatomical structure that passes through the anterior surface of the maxilla and opens at the infraorbital foramen, containing the infraorbital nerve, artery, and vein. Accurate localization of this canal in maxillofacial, dental implant, and orbital surgeries is of great importance to preventing nerve damage, reducing complications, and enabling successful surgical planning. The aim of this study is to perform automatic segmentation of the infraorbital canal in cone-beam computed tomography (CBCT) images using an artificial intelligence (AI)-based model. Methods: A total of 220 CBCT images of the IOC from 110 patients were labeled using the 3D Slicer software (version 4.10.2; MIT, Cambridge, MA, USA). The dataset was split into training, validation, and test sets at a ratio of 8:1:1. The nnU-Net v2 architecture was applied to the training and test datasets to predict and generate appropriate algorithm weight factors. The confusion matrix was used to check the accuracy and performance of the model. As a result of the test, the Dice Coefficient (DC), Intersection over the Union (IoU), F1-score, and 95% Hausdorff distance (95% HD) metrics were calculated. Results: By testing the model, the DC, IoU, F1-score, and 95% HD metric values were found to be 0.7792, 0.6402, 0.787, and 0.7661, respectively. According to the data obtained, the receiver operating characteristic (ROC) curve was drawn, and the AUC value under the curve was determined to be 0.91. Conclusions: Accurate identification and preservation of the IOC during surgical procedures are of critical importance to maintaining a patient’s functional and sensory integrity. The findings of this study demonstrated that the IOC can be detected with high precision and accuracy using an AI-based automatic segmentation method in CBCT images. This approach has significant potential to reduce surgical risks and to enhance the safety of critical anatomical structures.

Background/Objectives: There are various challenges in the segmentation of anatomical structures with artificial intelligence due to the different structural features of the relevant region/tissue. The aim of this study was to detect the nasolacrimal canal (NLC) using the nnU-Net v2 convolutional neural network (CNN) model in cone beam-computed tomography (CBCT) images and to evaluate the successful performance of the model in automatic segmentation. Methods: CBCT images of 100 patients were randomly selected from the data archive. The raw data were transferred to the 3D Slicer imaging software in DICOM format (Version 4.10.2; MIT, Massachusetts, USA). NLC was labeled using the polygonal type of manual method. The dataset was split into training, validation and test sets in a ratio of 8:1:1. nnU-Net v2 architecture was applied to the training and test datasets to predict and generate appropriate algorithm weight factors. The confusion matrix was used to check the accuracy and performance of the model. As a result of the test, the Dice Coefficient (DC), Intersection over Union (IoU), F1-Score and 95% Hausdorff distance (95% HD) metrics were calculated. Results: By testing the model, DC, IoU, F1-Scores and 95% HD metric values were found to be 0.8465, 0.7341, 0.8480 and 0.9460, respectively. According to the data obtained, the receiver-operating characteristic (ROC) curve was drawn and the AUC value under the curve was determined to be 0.96. Conclusions: These results showed that the proposed nnU-Net v2 model achieves NLC segmentation on CBCT images with high precision and accuracy. The automated segmentation of NLC may assist clinicians in determining the surgical technique to be used to remove lesions, especially those affecting the anterior wall of the maxillary sinus.