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    Automated 3D segmentation of the hyoid bone in CBCT using nnU-Net v2: a retrospective study on model performance and potential clinical utility
    (Bmc, 2025) Gumussoy, Ismail; Haylaz, Emre; Duman, Suayip Burak; Kalabalik, Fahrettin; Say, Seyda; Celik, Ozer; Bayrakdar, Ibrahim Sevki
    ObjectiveThis study aimed to identify the hyoid bone (HB) using the nnU-Net based artificial intelligence (AI) model in cone beam computed tomography (CBCT) images and assess the model's success in automatic segmentation.MethodsCBCT images of 190 patients were randomly selected. The raw data was converted to DICOM format and transferred to the 3D Slicer Imaging Software (Version 4.10.2; MIT, Cambridge, MA, USA). HB was labeled manually using the 3D Slicer. The dataset was divided into training, validation, and test sets in a ratio of 8:1:1. The nnU-Net v2 architecture was utilized to process the training and test datasets, generating the algorithm weight factors. To assess the model's accuracy and performance, a confusion matrix was employed. F1-score, Dice coefficient (DC), 95% Hausdorff distance (95% HD), and Intersection over Union (IoU) metrics were calculated to evaluate the results.ResultsThe model's performance metrics were as follows: DC = 0.9434, IoU = 0.8941, F1-score = 0.9446, and 95% HD = 1.9998. The receiver operating characteristic (ROC) curve was generated, yielding an AUC value of 0.98.ConclusionThe results indicated that the nnU-Net v2 model achieved high precision and accuracy in HB segmentation on CBCT images. Automatic segmentation of HB can enhance clinicians' decision-making speed and accuracy in diagnosing and treating various clinical conditions.Clinical trial numberNot applicable.
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    Automatic Segmentation of the Infraorbital Canal in CBCT Images: Anatomical Structure Recognition Using Artificial Intelligence
    (Mdpi, 2025) Gumussoy, Ismail; Haylaz, Emre; Duman, Suayip Burak; Kalabalik, Fahrettin; Eren, Muhammet Can; Say, Seyda; Celik, Ozer
    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.
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    Automatic Segmentation of the Nasolacrimal Canal: Application of the nnU-Net v2 Model in CBCT Imaging
    (Mdpi, 2025) Haylaz, Emre; Gumussoy, Ismail; Duman, Suayip Burak; Kalabalik, Fahrettin; Eren, Muhammet Can; Demirsoy, Mustafa Sami; Celik, Ozer
    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.
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    Morphological evaluation of maxillary and mandibular canines using cone-beam computed tomography in Turkish population
    (2019) Karataslioglu, Emrah; Kalabalik, Fahrettin
    Aim: The aim of the present study was to evaluate the root canal morphology of the maxillary and mandibular canines using cone-beam computed tomography (CBCT) in a Turkish population.Material and Methods: CBCT images of 377 maxillary and 419 mandibular canines were examined. The number of roots and their morphology, number of canals per root and canal configurations according to the Vertucci’s classification were recorded. Canal configurations and presence or absence of lateral canals were examined regarding gender, age groups, and tooth side. Anatomic symmetry in canal morphology of bilateral canines was also recorded. The data were evaluated by Pearson Chi-square test (p .05). Results:The prevalence of type I canal configuration in maxillary and mandibular canines was 96.3% and 87.8%, respectively. Twenty-one percent of the maxillary canines and 14.8% of the mandibular canines had accessory canals. In maxillary canines, no significant difference was observed in the prevalence of accessory canals between males and females and between age groups. In mandibular canines, lateral canals and complex canal morphology were detected significantly more often in females. Prevalence of complex root canal increased with age in both maxillary and mandibular canines and great anatomic symmetry was observed in canal morphology of bilaterally present teeth in the same patient.Conclusion: CBCT is a useful tool for evaluation of root and canal morphology. Our results can provide valuable aids for clinicians during root canal treatment of canines.
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    Morphometric Analysis of the Infraorbital Foramen in Children and Adolescents with Unilateral Cleft Lip and Palate: A CBCT Study
    (Mdpi, 2025) Haylaz, Emre; Kalabalik, Fahrettin; Gumussoy, Ismail; Duman, Suayip Burak; Eren, Muhammet Can; Say, Seyda; Akarcay, Furkan Osman
    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.