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    3D evaluation of cranial and dentofacial morphological differences between individuals with mouth breathing and nasal breathing
    (Elsevier, 2024) Topsakal, Kübra Gülnur; Yurdakurban, Ebru; Duran, Gökhan Serhat; Görgülü, Serkan
    Introduction: The present study aimed to identify the morphological differences in cranial and dentofacial structures between individuals with mouth-breathing and nasal-breathing. Materials and Methods: The study included 120 individuals, 60 each in the nasal breathing (NB) and mouth breathing (MB) groups. 3D stereophotogrammetry, lateral cephalometric radiographs, and intraoral examination results were recorded by the researchers to determine the morphological differences between the MB group and the NB group. The study utilized cephalometric radiographs for 2D hard tissue measurements and 3D stereophotogrammetric records for linear and angular measurements. Results: Statistically significant differences were found between the NB and MB groups' SNB angles (respectively, 79.3 +/- 3.04, 76.6 +/- 4.24, and p = 0.002). Also, the NB group's SN-GoGn angle was lower than the MB group's (respectively, 31.5 +/- 5.12, 36.0 +/- 5.55, and p = 0.002). Considering the Jarabak ratio, the NB group's Jarabak ratio was higher than the MB group (respectively,65.7 +/- 4.16, 62.6 +/- 4.10, and p = 0.014). In 3D stereophotogrammetry measurements, increased Li-Me' was detected in the MB group than in NB group. Conclusion: Mouth breathing results in significant morphological differences that affect the development of both soft tissues and skeletal structures. Orthodontists utilize these characteristic features observed in mouth-breathing anomalies for early diagnosis and consider referring their patients for medical treatment of mouth breathing. (c) 2024 Elsevier Masson SAS. All rights reserved.
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    Accuracy of deep learning-based upper airway segmentation
    (Elsevier, 2025) Süküt, Yağızalp; Yurdakurban, Ebru; Duran, Gökhan Serhat
    Introduction: In orthodontic treatments, accurately assessing the upper airway volume and morphology is essential for proper diagnosis and planning. Cone beam computed tomography (CBCT) is used for assessing upper airway volume through manual, semi-automatic, and automatic airway segmentation methods. This study evaluates upper airway segmentation accuracy by comparing the results of an automatic model and a semi-automatic method against the gold standard manual method. Materials and methods: An automatic segmentation model was trained using the MONAI Label framework to segment the upper airway from CBCT images. An open-source program, ITK-SNAP, was used for semi-automatic segmentation. The accuracy of both methods was evaluated against manual segmentations. Evaluation metrics included Dice Similarity Coefficient (DSC), Precision, Recall, 95% Hausdorff Distance (HD), and volumetric differences. Results: The automatic segmentation group averaged a DSC score of 0.915+0.041, while the semi-automatic group scored 0.940+0.021, indicating clinically acceptable accuracy for both methods. Analysis of the 95% HD revealed that semi-automatic segmentation (0.997+0.585) was more accurate and closer to manual segmentation than automatic segmentation (1.447+0.674). Volumetric comparisons revealed no statistically significant differences between automatic and manual segmentation for total, oropharyngeal, and velopharyngeal airway volumes. Similarly, no significant differences were noted between the semi-automatic and manual methods across these regions. Conclusion: It has been observed that both automatic and semi-automatic methods, which utilise opensource software, align effectively with manual segmentation. Implementing these methods can aid in decision-making by allowing faster and easier upper airway segmentation with comparable accuracy in orthodontic practice. (c) 2024 Elsevier Masson SAS. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
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    Assessment of deep learning technique for fully automated mandibular segmentation
    (Mosby-Elsevier, 2025) Yurdakurban, Ebru; Süküt, Yağızalp; Duran, Gökhan Serhat
    Introduction: This study aimed to assess the precision of an open-source, clinician-trained, and user-friendly convolutional neural network-based model for automatically segmenting the mandible. Methods: A total of 55 cone-beam computed tomography scans that met the inclusion criteria were collected and divided into test and training groups. The MONAI (Medical Open Network for Artificial Intelligence) Label active learning tool extension was used to train the automatic model. To assess the model's performance, 15 cone-beam computed tomography scans from the test group were inputted into the model. The ground truth was obtained from manual segmentation data. Metrics including the Dice similarity coefficient, Hausdorff 95%, precision, recall, and segmentation times were calculated. In addition, surface deviations and volumetric differences between the automated and manual segmentation results were analyzed. Results: The automated model showed a high level of similarity to the manual segmentation results, with a mean Dice similarity coefficient of 0.926 +/- 0.014. The Hausdorff distance was 1.358 +/- 0.466 mm, whereas the mean recall and precision values were 0.941 +/- 0.028 and 0.941 +/- 0.022, respectively. There were no statistically significant differences in the arithmetic mean of the surface deviation for the entire mandible and 11 different anatomic regions. In terms of volumetric comparisons, the difference between the 2 groups was 1.62 mm3, which was not statistically significant. Conclusions: The automated model was found to be suitable for clinical use, demonstrating a high degree of agreement with the reference manual method. Clinicians can use open-source software to develop custom automated segmentation models tailored to their specific needs. (Am J Orthod Dentofacial Orthop 2025;167:242-9)
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    Evaluating the accuracy between hollow and solid dental aligner models: a comparative study of printing technologies
    (Sciendo, 2024) Yurdakurban, Ebru; Topsakal, Kübra Gülnur; Duran, Gökhan Serhat; Görgülü, Serkan
    Objective To evaluate the accuracy between hollow and solid dental models produced using a StereoLithography Apparatus (SLA), Digital Light Processing (DLP), and PolyJet 3D printing technologies.Materials and methods Hollow (of 1 mm, 2 mm, 3 mm shell thicknesses) and solid maxillary models were produced using SLA, DLP, and PolyJet printers. To determine the accuracy of the tested models and deviations from the reference models, 3D digital superimposition was performed. For a detailed analysis, the dental arch was subdivided into five regions which yielded root mean square (RMS) values post-registration. Six different RMS values were generated, one for the total dental arch and one for each of the five individual regions. One-Way ANOVA analysis was applied for intergroup comparisons, and post hoc comparisons were conducted using the Tukey test. The significance of the deviation of RMS values from zero was evaluated through the one-sample t test.Results The PolyJet printer produced models with the least deviation for the total arch, while the SLA printer showed the greatest deviation. The DLP printer produced models with the least deviation for the hollow designs in the anterior region, while the SLA printer produced models with the least deviation of the solid design. The PolyJet printer showed the least deviation for both hollow and solid designs of 2 mm and 3 mm shell thicknesses in the molar regions. Except for the 1 mm shell thickness hollow design on the right side, the PolyJet printer showed the highest accuracy in the premolar-canine regions.Conclusion Accuracy varies in the posterior and anterior regions of the dental arch as a result of different shell thicknesses produced by 3D printing technologies. The clinician should select a design that is appropriate for the intended 3D printing technology based on use and required accuracy.