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Öğe 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 ZakirObjectiveAccurate 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.Öğe 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 SevkiObjectives 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.Öğe 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 SevkiObjectiveThis 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.Öğe 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, OzerThe 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.Öğe Automatic Feature Segmentation in Dental Periapical Radiographs(Mdpi, 2022) Ari, Tugba; Saglam, Hande; Oksuzoglu, Hasan; Kazan, Orhan; Bayrakdar, Ibrahim Sevki; Duman, Suayip Burak; Celik, OzerWhile 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.Öğe 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, OzerBackgroundMaxillofacial 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.Öğe 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, OzerBackgroundThe 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.Öğe Cone beam computed tomography imaging of ponticulus posticus: prevalence, characteristics, and a review of the literature(Elsevier Science Inc, 2014) Bayrakdar, Ibrahim Sevki; Miloglu, Ozkan; Altun, Oguzhan; Gumussoy, Ismail; Durna, Dogan; Yilmaz, Ahmet BerhanObjective. The aim of this study was to investigate the frequency of ponticulus posticus (PP) using cone beam computed tomography (CBCT) and to describe the radiologic characteristics of the detected cases. Study Design. The presence and types of PP were investigated on 730 CBCT images. Results. PP was found in 17.4% (127) of the 730 CBCT scans. Of these 127 patients, 79 (10.8%) had bilateral PP and 48 (6.6%) had unilateral PP. Male predominance was found with a prevalence of 19.5% (54 of 277) and female prevalence was 16.1% (73 of 453). The prevalence of PP increased with age; the highest prevalence of PP was seen in those who were 49 to 81 years of age. Conclusions. This study shows that PP is not an uncommon anatomic variation and is a natural incidental finding on CBCT.Öğe 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 GunenObjective: 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.Öğe 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, YasinThe 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.Öğe 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, OzerThe 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.Öğe Detecting the presence of taurodont teeth on panoramic radiographs using a deep learning-based convolutional neural network algorithm(Springer, 2023) Duman, Sacide; Yilmaz, Emir Faruk; Eser, Gozde; Celik, Ozer; Bayrakdar, Ibrahim Sevki; Bilgir, Elif; Ferreira Costa, Andre LuizObjectives Artificial intelligence (AI) techniques like convolutional neural network (CNN) are a promising breakthrough that can help clinicians analyze medical imaging, diagnose taurodontism, and make therapeutic decisions. The purpose of the study is to develop and evaluate the function of CNN-based AI model to diagnose teeth with taurodontism in panoramic radiography. Methods 434 anonymized, mixed-sized panoramic radiography images over the age of 13 years were used to develop automatic taurodont tooth segmentation models using a Pytorch implemented U-Net model. Datasets were split into train, validation, and test groups of both normal and masked images. The data augmentation method was applied to images of trainings and validation groups with vertical flip images, horizontal flip images, and both flip images. The Confusion Matrix was used to determine the model performance. Results Among the 43 test group images with 126 labels, there were 109 true positives, 29 false positives, and 17 false negatives. The sensitivity, precision, and F1-score values of taurodont tooth segmentation were 0.8650, 0.7898, and 0.8257, respectively. Conclusions CNN's ability to identify taurodontism produced almost identical results to the labeled training data, and the CNN system achieved close to the expert level results in its ability to detect the taurodontism of teeth.Öğe 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, IsmailObjectives 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.Öğe 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]Öğe Evaluation of Sella Turcica Shape and Dimensions in Cleft Subjects Using Cone-Beam Computed Tomography(Karger, 2017) Yasa, Yasin; Bayrakdar, Ibrahim Sevki; Ocak, Ali; Duman, Suayip Burak; Dedeoglu, NumanObjective: The aim of this study was to assess the morphology of the sella turcica and measure its size in cleft and noncleft subjects. Material and Methods: Cone-beam computed tomography (CBCT) images of 54 individuals (29 males; 25 females) with cleft and 85 (22 males; 63 females) without cleft were used for this study. Syndromic patients with cleft(s) were not included because of possible additional endocrinological and/or morphological disorders. Linear measurements included length, depth, and diameter. The shape of the sella turcica was analyzed in the cleft and noncleft groups. An independent t test was conducted to evaluate differences between genders and groups. One-way ANOVA was used to compare age groups. Results: The length (p < 0.001) of the sella turcica was smaller in noncleft subjects than in cleft subjects. Diameter (p = 0.014) and depth (p = 0.005) showed as constantly increasing from an age < 15 to >25 years in the overall assessment. The distribution of the shape of the sella turcica differed significantly between groups (p < 0.001). Conclusions: In this study, CBCT was used to assess the morphology of the sella turcica. A majority of the subjects with cleft had a flattened sella turcica compared to that of the control group. A shorter length of the sella turcica was more evident in the cleft subjects than in the control group. (C) 2016 S. Karger AG, BaselÖğe Introduction to Large Language Models and the Application of Generative Artifical Intelligence in Dental Education and Clinical Practice(W.B. Saunders, 2026) Syed, Ali; Duman, Suayip Burak; Ozen, Duygu Celik; Bayrakdar, Ibrahim Sevki; Mupparapu, Mel[No abstract available]Öğe Morphologic Evaluations of Hypoglossal Canal using Cone Beam Computed Tomography(Univ Kebangsaan Malaysia, 2021) Duman, Suayip Burak; Seyrek, Mehmet; Yasa, Yasin; Gumussoy, Ismail; Dedeoglu, Numan; Bayrakdar, Ibrahim SevkiHypoglossal canal (HC) which begins from very slightly above the inner part of the anterolateral portion of the foramen magnum and is located above the occipital condyle of the occipital bone. The aim of this study is to examine HC morphology and variations using cone beam computed tomography (CBCT). The morphology and types of HC were investigated with 303 CBCT images (606 side). type 1 variation in 606 HC examined becomes the most commonly observed type (57.3%) while type 5 variation was the least common type of variation (0.8%). Type 1 BC was statistically higher in males (p=0.004). Because of HC, which is an anthropologically important point and enters the field of images in CBCT scan, it is recommended that dental radiologists should be aware of their variations and be wary of the pathologies that may occur in this region.Öğe Morphometric Analysis of Sella Turcica Using Cone Beam Computed Tomography(Lippincott Williams & Wilkins, 2017) Yasa, Yasin; Ocak, Ali; Bayrakdar, Ibrahim Sevki; Duman, Suayip Burak; Gumussoy, IsmailObjective: The purpose of this study was to assess morphological shape and morphometric analysis of the sella turcica using cone beam computed tomography (CBCT) in different planes of section (coronal and sagittal). Materials and Methods: CBCT images of 177 subjects of which 51 males and 126 females in the age group of 11 to 73 years were included in the study population. Linear dimensions which include the length, depth, diameter, and interclinoid distance were measured and the shape of sella turcica was analyzed. Results: Sella turcica had circular morphology in 69.5% of the subjects while flattened shape of sella turcica was observed in 16.4%, oval shape of sella turcica in 14%. There was no significant difference in the all measurements of sella turcica between males and females (P > 0.05). Diameter (P < 0.01), depth (P < 0.001), length (P < 0.05), and interclinoid distance (P < 0.05) of the sella turcica differed significantly with age. Conclusions: The anatomical structure of sella turcica can be studied effectively in CBCT images. Linear dimensions and shape of sella turcica in the current study can be used as reference standards for further investigations.Öğe Morphometric and morphological evaluation of mastoid emissary canal using cone-beam computed tomography(Sage Publications Ltd, 2023) Temiz, Mustafa; Ozen, Duygu Celik; Duman, Suayip Burak; Bayrakdar, Ibrahim Sevki; Kazan, Orhan; Jagtap, Rohan; Altun, OguzhanObjectives:This study aimed to determine mastoid emissary canal's (MEC) and mastoid foramen (MF) prevalence and morphometric characteristics on cone-beam computed tomography (CBCT) images to underline its clinical significance and discuss its surgical consequences. Methods:In the retrospective analysis, two oral and maxillofacial radiologists analyzed the CBCT images of 135 patients (270 sides). The biggest MF and MEC were measured in the images evaluated in MultiPlanar Reconstruction (MPR) views. The MF and MEC mean diameters were calculated. The mastoid foramina number was recorded. The prevalence of MF was studied according to gender and side of the patient. Results:The overall prevalence of MEC and MF was 119 (88.1%). The prevalence of MEC and MF is 55.5% in females and 44.5% in males. MEC and MF were identified as bilateral in 80 patients (67.20%) and unilateral in 39 patients (32.80%). The mean diameter of MF was 2.4 +/- 0.9 mm. The mean height of MF was 2.3 +/- 0.9. The mean diameter of the MEC was 2.1 +/- 0.8, and the mean height of the MEC was 2.1 +/- 0.8. There is a statistical difference between the genders (p = 0.043) in foramen diameter. Males had a significantly larger mean diameter of MF in comparison to females. Conclusion:MEC and MF must be evaluated thoroughly if the surgery is contemplated. Radiologists and surgeons should be aware of mastoid emissary canal morphology, variations, clinical relevance, and surgical consequences while operating in the suboccipital and mastoid areas to avoid unexpected and catastrophic complications. CBCT may be a reliable imaging diagnostic technique.Öğe Nasolabial Cyst: A Case Report with Ultrasonography and Magnetic Resonance Imaging Findings(Hindawi Ltd, 2017) Ocak, Ali; Duman, Suayip Burak; Bayrakdar, Ibrahim Sevki; Cakur, BinaliNasolabial cysts are uncommon nonodontogenic lesions that occur in the nasal alar region. These lesions usually present with asymptomatic swelling but can cause pain if infected. In this case report, we describe the inadequacy of conventional radiography in a nasolabial cyst case, as well as the magnetic resonance imaging (MRI) and ultrasonography (US) findings in a 54-year-old female patient.
