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AI-powered segmentation of bifid mandibular canals using CBCT
(Bmc, 2025) Gumussoy, Ismail; Demirezer, Kardelen; Duman, Suayip Burak; Haylaz, Emre; Bayrakdar, Ibrahim Sevki; Celik, Ozer; Syed, Ali Zakir
ObjectiveAccurate segmentation of the mandibular and bifid canals is crucial in dental implant planning to ensure safe implant placement, third molar extractions and other surgical interventions. The objective of this study is to develop and validate an innovative artificial intelligence tool for the efficient, and accurate segmentation of the mandibular and bifid canals on CBCT.Materials and methodsCBCT data were screened to identify patients with clearly visible bifid canal variations, and their DICOM files were extracted. These DICOM files were then imported into the 3D Slicer (R) open-source software, where bifid canals and mandibular canals were annotated. The annotated data, along with the raw DICOM files, were processed using the nnU-Netv2 training model by CranioCatch AI software team.Results69 anonymized CBCT volumes in DICOM format were converted to NIfTI file format. The method, utilizing nnU-Net v2, accurately predicted the voxels associated with the mandibular canal, achieving an intersection of over 50% in nearly all samples. The accuracy, Dice score, precision, and recall scores for the mandibular canal/bifid canal were determined to be 0.99/0.99, 0.82/0.46, 0.85/0.70, and 0.80/0.42, respectively.ConclusionsDespite the bifid canal segmentation not meeting the expected level of success, the findings indicate that the proposed method shows promising and has the potential to be utilized as a supplementary tool for mandibular canal segmentation. Due to the significance of accurately evaluating the mandibular canal before surgery, the use of artificial intelligence could assist in reducing the burden on practitioners by automating the complicated and time-consuming process of tracing and segmenting this structure.Clinical relevanceBeing able to distinguish bifid channels with artificial intelligence will help prevent neurovascular problems that may occur before or after surgery.
Artificial intelligence-based fully automatic 3D paranasal sinus segmentation
(Oxford Univ Press, 2026) Yigit, Meryem Kaygisiz; Pinarbasi, Alp; Etoz, Meryem; Duman, Suayip Burak; Bayrakdar, Ibrahim Sevki
Objectives Precise 3D segmentation of paranasal sinuses is essential for accurate diagnosis and treatment. This study aimed to develop a fully automated segmentation algorithm for the paranasal sinuses using the nnU-Net v2 architecture.Methods The nnU-Net v2-based segmentation algorithm was developed using Python 3.6.1 and the PyTorch library, and its performance was evaluated on a dataset of 97 cone beam CT (CBCT) scans. Ground truth annotations were manually generated by expert radiologists using the 3D Slicer software, employing a polygonal labelling technique across sagittal, coronal, and axial planes. Model performance was assessed using several quantitative metrics, including accuracy, Dice coefficient (DC), sensitivity, precision, Jaccard index, area under the curve (AUC), and 95% Hausdorff distance (95% HD).Results The nnU-Net v2-based algorithm demonstrated high segmentation performance across all paranasal sinuses. DC values were 0.94 for the frontal, 0.95 for the sphenoid, 0.97 for the maxillary, and 0.88 for the ethmoid sinuses. Accuracy scores exceeded 99% for all sinuses. The 95% HD values were 0.51 mm for both the frontal and maxillary sinuses, 0.85 mm for the sphenoid sinus, and 1.17 mm for the ethmoid sinus. Jaccard indices were 0.90, 0.91, 0.94, and 0.80, respectively.Conclusions This study highlights the high accuracy and precision of the nnU-Net v2-based CNN model in the fully automated segmentation of paranasal sinuses from CBCT images. The results suggest that the proposed model can significantly contribute to clinical decision-making processes, facilitating diagnostic and therapeutic procedures.
Assesment of Prelacrimal Recess in Patients With Maxillary Sinus Hypoplasia Using Cone Beam Computed Tomography
(Sage Publications Inc, 2021) Duman, Suayip Burak; Gumussoy, Ismail
Background The prelacrimal recess approach, is frequently preferred in creating a minimally invasive surgical corridors. Objective The aim of this study was to evaluate the Prelacrimal recess (PLR) anatomy using Cone Beam Computed Tomography in patients with Maxillary Sinus Hypoplasia. Methods The paranasal Cone Beam Computed Tomography series of 84 adults were analyzed retrospectively. The antero-posterior and mesio-distal widths of the PLR and the antero-posterior width of the naso-lacrimal duct were measured. The patients were divided into three groups according to the antero-posterior width of PLR to evaluate the feasibility of prelacrimal recess approach as Type 1 (0-3 mm), Type 2 (>3-7 mm) and Type 3 (>7 mm). Results The mean antero-posterior width of PLR was 3.11 +/- 1.49mm in the patients and 4.77 +/- 1.76 mm in the controls. The mean mesio-distal width of PLR was 7.64 +/- 1.49 mm in the patients and 3.17 +/- 2.05 mm in the controls. The mean antero-posterior width of naso-lacrimal duct was 9.58 +/- 2.80 mm in the patients and 9.46 +/- 2.42 mm in the controls. Conclusions The width of the antero-posterior PLR in patients with Maxillary Sinus Hypoplasia was found to be significantly lower in comparison to individuals with normal maxillary sinuses in the measurements performed on paranasal Cone Beam Computed Tomography scans. Hence, while planning a Functional Endoscopic Sinus Surgery with prelacrimal recess approach for maxillary sinus, the anatomical structure of the naso-sinusoidal region should be carefully analyzed, and individual anatomical variations such as Maxillary Sinus Hypoplasia should not be ignored.
Assessment of The Effects of Edentulousness on Temporomandibular Components by Using Cone Beam Computed Tomography
(Univ Indonesia, Fac Dentistry, 2022) Arikan, Busra; Dedeoglu, Numan; Duman, Suayip Burak
Objective: The aim of this study is to evaluate the effects of edentulousness on the temporomandibular joint using cone beam computed tomography (CBCT). Methods: In this study, CBCT images were evaluated in a total of 48 patients (24 dentulous, 24 edentulous). Ninety-six temporomandibular joint CBCT images were examined. Eminence inclination, condyle head widths and joint space were measured and statistically compared between the edentulous and dentulous groups. Results: The articular eminence inclination value the mediolateral width of the condyle and the anteroposterior width of the condyle were found significantly higher in the dentulous group than in the edentulous group. There was no significant difference between the dentulous and edentulous groups in terms of the anterior, superior and posterior joint space. Conclusion: In the edentulous patients, the articular eminence inclination value, mediolateral and anteroposterior widths of the condyle head were found to lower in comparison to the dentulous patients.
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.
Automated Mesiodens Detection with Deep-Learning-Based System Using Cone-Beam Computed Tomography Images
(Wiley-Hindawi, 2023) Syed, Ali Zakir; Ozen, Duygu Celik; Abdelkarim, Ahmed Z.; Duman, Suayip Burak; Bayrakdar, Ibrahim Sevki; Duman, Sacide; Celik, Ozer
The detection of mesiodens supernumerary teeth is crucial for appropriate diagnosis and treatment. The study aimed to develop a convolutional neural network (CNN)-based model to automatically detect mesiodens in cone-beam computed tomography images. A datatest of anonymized 851 axial slices of 106 patients' cone-beam images was used to process the artificial intelligence system for the detection and segmentation of mesiodens. The CNN model achieved high performance in mesiodens segmentation with sensitivity, precision, and F1 scores of 1, 0.9072, and 0.9513, respectively. The area under the curve (AUC) was 0.9147, indicating the model's robustness. The proposed model showed promising potential for the automated detection of mesiodens, providing valuable assistance to dentists in accurate diagnosis.
Automatic Feature Segmentation in Dental Periapical Radiographs
(Mdpi, 2022) Ari, Tugba; Saglam, Hande; Oksuzoglu, Hasan; Kazan, Orhan; Bayrakdar, Ibrahim Sevki; Duman, Suayip Burak; Celik, Ozer
While a large number of archived digital images make it easy for radiology to provide data for Artificial Intelligence (AI) evaluation; AI algorithms are more and more applied in detecting diseases. The aim of the study is to perform a diagnostic evaluation on periapical radiographs with an AI model based on Convoluted Neural Networks (CNNs). The dataset includes 1169 adult periapical radiographs, which were labelled in CranioCatch annotation software. Deep learning was performed using the U-Net model implemented with the PyTorch library. The AI models based on deep learning models improved the success rate of carious lesion, crown, dental pulp, dental filling, periapical lesion, and root canal filling segmentation in periapical images. Sensitivity, precision and F1 scores for carious lesion were 0.82, 0.82, and 0.82, respectively; sensitivity, precision and F1 score for crown were 1, 1, and 1, respectively; sensitivity, precision and F1 score for dental pulp, were 0.97, 0.87 and 0.92, respectively; sensitivity, precision and F1 score for filling were 0.95, 0.95, and 0.95, respectively; sensitivity, precision and F1 score for the periapical lesion were 0.92, 0.85, and 0.88, respectively; sensitivity, precision and F1 score for root canal filling, were found to be 1, 0.96, and 0.98, respectively. The success of AI algorithms in evaluating periapical radiographs is encouraging and promising for their use in routine clinical processes as a clinical decision support system.
Automatic maxillary sinus segmentation and pathology classification on cone-beam computed tomographic images using deep learning
(Bmc, 2024) Altun, Oguzhan; Ozen, Duygu Celik; Duman, Suayip Burak; Dedeoglu, Numan; Bayrakdar, Ibrahim Sevki; Eser, Gozde; Celik, Ozer
BackgroundMaxillofacial complex automated segmentation could alternative traditional segmentation methods to increase the effectiveness of virtual workloads. The use of DL systems in the detection of maxillary sinus and pathologies will both facilitate the work of physicians and be a support mechanism before the planned surgeries.ObjectiveThe aim was to use a modified You Only Look Oncev5x (YOLOv5x) architecture with transfer learning capabilities to segment both maxillary sinuses and maxillary sinus diseases on Cone-Beam Computed Tomographic (CBCT) images.MethodsData set consists of 307 anonymised CBCT images of patients (173 women and 134 males) obtained from the radiology archive of the Department of Oral and Maxillofacial Radiology. Bilateral maxillary sinuses CBCT scans were used to identify mucous retention cysts (MRC), mucosal thickenings (MT), total and partial opacifications, and healthy maxillary sinuses without any radiological features.ResultsRecall, precision and F1 score values for total maxillary sinus segmentation were 1, 0.985 and 0.992, respectively; 1, 0.931 and 0.964 for healthy maxillary sinus segmentation; 0.858, 0.923 and 0.889 for MT segmentation; 0.977, 0.877 and 0.924 for MRC segmentation; 1, 0.942 and 0.970 for sinusitis segmentation.ConclusionThis study demonstrates that maxillary sinuses can be segmented, and maxillary sinus diseases can be accurately detected using the AI model.
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.
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.
Classification of temporomandibular joint osteoarthritis on cone beam computed tomography images using artificial intelligence system
(Wiley, 2023) Eser, Gozde; Duman, Suayip Burak; Bayrakdar, Ibrahim Sevki; Celik, Ozer
BackgroundThe use of artificial intelligence has many advantages, especially in the field of oral and maxillofacial radiology. Early diagnosis of temporomandibular joint osteoarthritis by artificial intelligence may improve prognosis. ObjectiveThe aim of this study is to perform the classification of temporomandibular joint (TMJ) osteoarthritis and TMJ segmentation on cone beam computed tomography (CBCT) sagittal images with artificial intelligence. ResultsThe sensitivity, precision and F1 scores of the model for TMJ osteoarthritis classification are 1, 0.7678 and 0.8686, respectively. The accuracy value for classification is 0.7678. The prediction values of the classification model are 88% for healthy joints, 70% for flattened joints, 95% for joints with erosion and 86% for joints with osteophytes. The sensitivity, precision and F1 score of the YOLOv5 model for TMJ segmentation are 1, 0.9953 and 0.9976, respectively. The AUC value of the model for TMJ segmentation is 0.9723. In addition, the accuracy value of the model for TMJ segmentation was found to be 0.9953. ConclusionArtificial intelligence model applied in this study can be a support method that will save time and convenience for physicians in the diagnosis of the disease with successful results in TMJ segmentation and osteoarthritis classification.
Clinical significance of maxillary sinus hypoplasia in dentistry: A CBCT study
(Polish Dental Soc, 2020) Dedeoglu, Numan; Duman, Suayip Burak
Background. The anatomy of the maxillary sinus is especially important for dentists due to the close proximity of the sinus to the maxillary posterior teeth. Objectives. The aim of the present study was to investigate the frequency of maxillary sinus pathology, anatomical variations, and the relationship between the tooth roots and the maxillary sinus by comparing a group with maxillary sinus hypoplasia (MSH) and a control group using cone-beam computed tomography (CBCT). Material and methods. In the study, 69 CBCT images of 50 patients with MSH and 84 CBCT images of 49 patients without MSH were evaluated for pathology, and the presence of an accessory ostium, a septum and Haller cells in each maxillary sinus. Results. The coincidence of pathology with MSH was 29%, and with non-hypoplastic maxillary sinuses it was 44% (p = 0.055). An accessory ostium was found in 14.5% of scans with MSH and in 39.3% of those without MSH (p = 0.001). Haller cells were found in 2.9% of the MSH cases, whereas their incidence in the control group was 23.8% (p = 0.000). The occurrence of a sinus septum was at the level of 4.3% in the group with MSH and 23.8% in the group without MSH (p = 0.001). Conclusions. The incidence of the relationship between the sinus wall and the posterior root apices was found smaller in the dentulous MSH patients. Also, the distance between the root apices and the sinus wall was longer in the dentulous MSH patients, and the vertical and horizontal alveolar bone was larger in the posteriorly edentulous MSH patients.
Comparative Evaluation of Temporomandibular Joint Parameters in Unilateral and Bilateral Cleft Lip and Palate Patients Using Cone-Beam CT: Focus on Growing vs. Non-Growing Subjects
(Mdpi, 2024) Abdelkarim, Ahmed Z.; Almeshari, Ahmed A.; Ozen, Duygu Celik; Khalifa, Ayman R.; Rezallah, Nader N.; Duman, Suayip Burak; Khurana, Sonam
Background: Morphological differences in the temporomandibular joint (TMJ) are crucial for the treatment of patients with cleft lip and palate (CLP). This study aims to evaluate and compare the TMJ parameters in patients with unilateral and bilateral CLP across growing and non-growing age groups using cone-beam computed tomography (CBCT). Methods: CBCT records from 57 patients (23 males and 34 females) aged 6-50 years with a diagnosed unilateral or bilateral CLP were analyzed. Patients were categorized into four groups: growing unilateral (UGCLP), growing bilateral (BGCLP), non-growing unilateral (UNGCLP), and non-growing bilateral (BNGCLP). Measurements of TMJ parameters, including the mandibular fossa, articular eminence inclination, joint spaces, and roof thickness of the glenoid fossa, were conducted using CBCT images. Results: Significant differences were observed in the anterior joint space (AJS) and the roof of the glenoid fossa (RGF) between growing and non-growing unilateral cleft patients. Additionally, significant discrepancies were found in the articular eminence angle when comparing the cleft and non-cleft sides within the unilateral growing group. No significant differences were observed in TMJ parameters between the right and left sides among bilateral cleft patients. Conclusions: The study highlights distinct TMJ morphological differences between growing and non-growing patients with CLP, emphasizing the importance of age-specific considerations in the treatment planning and growth monitoring of these patients.
Comparative Morphometric Study of the Occipital Condyle in Class III and Class I Skeletal Malocclusion Patients
(Mdpi, 2024) Gumussoy, Ismail; Duman, Suayip Burak; Miloglu, Ozkan; Demirsoy, Mustafa Sami; Dogan, Ayhan; Abdelkarim, Ahmed Z.; Guller, Mustafa Taha
Objectives: Since the formation of skeletal malocclusions is closely linked to general craniofacial development, it is crucial to understand the anatomy and growth patterns of the skull base. This study aimed to assess the morphometry of the occipital condyle (OC) on CBCT scans of Class III skeletal malocclusion subjects and compare the findings with those of skeletal Class I malocclusion subjects. Methods: A retrospective analysis was performed on CBCT images based on predefined inclusion and exclusion criteria. The sample consisted of 76 CBCT images of 38 skeletal Class III patients and 38 skeletal Class I patients. CBCT scans were used to measure mesiodistal width, sagittal length, coronal height, effective height of OC, and sagittal OC angle. Statistical analyses were conducted with RStudio software. Results: Significant differences were found in sagittal OC angle and sagittal length of OC between the study groups (p < 0.001). In other metrics, such as coronal height of OC, effective OC height, and mesiodistal width of OC between the groups, no significant differences were found. Class III malocclusions exhibited significantly reduced sagittal OC angle and sagittal length of OC compared to Class I malocclusions. The left side showed a significantly larger sagittal OC angle than the right side (p = 0.002). Conclusions: This preliminary study identified reduced sagittal angle and sagittal length of OC in patients with Class III skeletal malocclusion. Clinicians should recognize potential differences in OC morphometry in patients with skeletal malocclusions. Future studies involving larger populations are recommended to further investigate the relationship between skeletal malocclusions and posterior cranial base structures, including the OC.
Cone beam computed tomography imaging of superior semicircular canal morphology: a retrospective comparison of cleft lip/palate patients and normal controls
(Taylor & Francis Ltd, 2018) Altun, Oguzhan; Duman, Suayip Burak; Bayrakdar, Ibrahim Sevki; Yasa, Yasin; Duman, Sacide; Yilmaz, Sevcihan Gunen
Objective: This study evaluated the prevalence and morphological characteristics of the superior semicircular canal (SSCC) in cleft lip and palate (CUP) patients using cone beam computed tomography (CBCT). Materials and methods: CBCT images of 53CL/P patients (28 males and 25 females) and a control group of 76 patients (42 males and 34 females) were evaluated. Retrospectively, 258 temporal bone images from 129 patients were evaluated in terms of SSCC morphology and divided into a normal pattern (0.6-1.7mm in thickness), a papyraceous pattern (<0.5mm), a thick pattern (>1.8mm), a pneumatized pattern and dehiscent. The chi-squared test was used to compare differences among semicircular canal dehiscence (SSCD) patterns in the CL/P and control groups; p <.05 was taken to reflect statistical significance. Results: The characteristics of the SSCC were evaluated on CBCT images in patients with CL/P and controls. In total, 158 (61%) cases were normal (0.6-1.7mm in thickness), 31 (12%) papyraceous (<0.5 mm), 8 (3%) thick, and 34 (13%) pneumatized. SSCD was observed in 27 (11%) cases. Statistically significant differences between the CL/P and control groups were evident in terms of SSCC morphology (p<.001). Conclusions: SSCD should be considered if a CL/P patient exhibits a vestibular system deficiency. Oral and maxillofacial radiologists should pay attention to SSCD when interpreting CBCT images. Future studies should use high-level spatial resolution CBCT to focus on cleft site and SSCC morphology in larger patient populations.
Cone-Beam Computed Tomography Evaluation of the Eustachian Tube in Patients With Cleft Lip and Palate Compared With Normal Controls
(Lippincott Williams & Wilkins, 2020) Duman, Suayip Burak; Bayrakdar, Ibrahim Sevki; Yasa, Yasin
The authors compared the morphological features of the Eustachian tube (ET) between patients with cleft lip and palate (CL/P) and normal controls using cone-beam computed tomography (CBCT). CBCT images of 51 CL/P patients (28 males and 23 females, mean age: 18.5 +/- 8.0 years) and a control group of 52 patients (22 males and 30 females, mean age: 25.23 +/- 10.65 years) were retrospectively evaluated. The Eustachian tube angle (ETA), Eustachian tube length (EL), and auditory tube angle (ATA) were measured on CBCT images. The ETA, EL, and ATA in the CL/P and normal control groups were 30.4 +/- 6.2 and 36.7 +/- 7.5 degrees; 24.7 +/- 3.7 and 27.7 +/- 4.3 mm; and 142.4 +/- 7.8 and 136.3 +/- 4.1 degrees, respectively. All between-group differences were statistically significant (allP < 0.05). There were no significant between-gender differences in either group (allP > 0.05). Continuous variables were compared using the Mann-Whitney U-test. The morphological features of the ET, measured via multiplanar reconstruction CBCT, differed between CL/P patients and normal controls. CBCT can be used to evaluate ET morphological features.
Convolutional Neural Network Performance for Sella Turcica Segmentation and Classification Using CBCT Images
(Mdpi, 2022) Duman, Suayip Burak; Syed, Ali Z.; Ozen, Duygu Celik; Bayrakdar, Ibrahim Sevki; Salehi, Hassan S.; Abdelkarim, Ahmed; Celik, Ozer
The present study aims to validate the diagnostic performance and evaluate the reliability of an artificial intelligence system based on the convolutional neural network method for the morphological classification of sella turcica in CBCT (cone-beam computed tomography) images. In this retrospective study, sella segmentation and classification models (CranioCatch, Eskisehir, Turkiye) were applied to sagittal slices of CBCT images, using PyTorch supported by U-Net and TensorFlow 1, and we implemented the GoogleNet Inception V3 algorithm. The AI models achieved successful results for sella turcica segmentation of CBCT images based on the deep learning models. The sensitivity, precision, and F-measure values were 1.0, 1.0, and 1.0, respectively, for segmentation of sella turcica in sagittal slices of CBCT images. The sensitivity, precision, accuracy, and F1-score were 1.0, 0.95, 0.98, and 0.84, respectively, for sella-turcica-flattened classification; 0.95, 0.83, 0.92, and 0.88, respectively, for sella-turcica-oval classification; 0.75, 0.94, 0.90, and 0.83, respectively, for sella-turcica-round classification. It is predicted that detecting anatomical landmarks with orthodontic importance, such as the sella point, with artificial intelligence algorithms will save time for orthodontists and facilitate diagnosis.
Evaluation of Lefort I Osteotomy Line and Pterygomaxillary Junction Region in Patients With Cleft Lip and Palate
(Sage Publications Inc, 2021) Sancar, Bahadir; Duman, Suayip Burak
Objective: This study aimed to evaluate the Le Fort I osteotomy line and pterygomaxillary junction via cone-beam computed tomography in individuals with cleft lip and palate (CLP). Design: Retrospective study. Patients and Methods: The study included individuals older than 16 years with CLP, who were scheduled for repositioning of the maxilla by Le Fort I osteotomy, and those with class III malocclusion with maxillary hypoplasia, who were scheduled for Le Fort I osteotomy. The measurements made in the area of the cleft of individuals with CLP were compared with both the side with no cleft and those with class III malocclusion with maxillary hypoplasia. A total of 11 measurements were made on the axial section parallel to the Frankfurt Horizontal plane, corresponding to the lower 1/5 of the distance between the infraorbital foramen and the anterior nasal spine. Results: There were significant differences both in the comparisons made between the individuals with CLP and those without CLP in terms of the canal-anterior alveolar crest (G) and sinus-anterior alveolar crest (L) measurements (P < .05). The mean measurement values showed that the measurement results were higher in individuals with CLP in general. Conclusion: In conclusion, we believe that there might be difficulties both in osteotomy and down fracture stages during Le Fort I osteotomies performed in individuals with CLP.
Evaluation of radix entomolaris in mandibular first and second molars using cone-beam computed tomography and review of the literature
(Springer, 2020) Duman, Suayip Burak; Duman, Sacide; Bayrakdar, Ibrahim Sevki; Yasa, Yasin; Gumussoy, Ismail
Objectives The aim of the present study is to identify the prevalence of radix entomolaris in mandibular first and second molars and to determine morphological classifications and associations with other root and canals. Methods Mandibular first and second molar teeth of 850 Turkish patients were evaluated using cone-beam computed tomography. A total of 2800 mandibular first molars and second molars were screened. The CBCT examination was performed at five different axial levels. The prevalence of total radix entomolaris, unilateral-bilateral, right-left side and gender distributions, and the classification of radix entomolaris's canal configurations were measured. Results Radix entomolaris was found in 2.9% (n = 25) of the patients and 1.2% (n = 34) of the teeth. The prevalence of radix entomolaris in mandibular first molars was higher than in mandibular second molars (p < 0.01), in males than in females (p < 0.05) and in right side than left side. An additional tubercle was found in 23% of the teeth with radix entomolaris. For buccolingual orientation, Type A canal variation was the highest and Type C canal variation was the lowest. Regarding locations of cervical parts, Type III canal variation was the highest while Type I canal variation was the lowest. Conclusions The prevalence of radix entomolaris was lower in the Turkish population than in other Asian populations but, in multiethnic societies, it needs attention. Before starting endodontic treatment, the clinician should examine the radiography thoroughly and apply advanced radiography methods when necessary. Cone-beam computed tomography is a valuable advanced radiography method for assessing such anatomical variations in vivo.
Evaluation of radix entomolaris in mandibular first and second molars using cone-beam computed tomography and review of the literature (vol 36, pg 320, 2020)
(Springer, 2022) Duman, Suayip Burak; Duman, Sacide; Bayrakdar, Ibrahim Sevki; Yasa, Yasin; Gumussoy, Ismail
[Abstract Not Available]

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