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Objective: The aim of this study is to determine if a deep learning (DL) model can predict the surgical difficulty for impacted maxillary third molar tooth using panoramic images before surgery., Materials and Methods: The dataset consists of 708 panoramic radiographs of the patients who applied to the Oral and Maxillofacial Surgery Clinic for various reasons. Each maxillary third molar difficulty was scored based on dept (V), angulation (H), relation with maxillary sinus (S), and relation with ramus (R) on panoramic images. The YoloV5x architecture was used to perform automatic segmentation and classification. To prevent re-testing of images, participate in the training, the data set was subdivided as: 80 % training, 10 % validation, and 10 % test group., Results: Impacted Upper Third Molar Segmentation model showed best success on sensitivity, precision and F1 score with 0,9705, 0,9428 and 0,9565, respectively. S-model had a lesser sensitivity, precision and F1 score than the other models with 0,8974, 0,6194, 0,7329, respectively., Conclusion: The results showed that the proposed DL model could be effective for predicting the surgical difficulty of an impacted maxillary third molar tooth using panoramic radiographs and this approach might help as a decision support mechanism for the clinicians in peri‑surgical period., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Masson SAS. All rights reserved.)

Objective: To investigate the effectiveness of using YOLO-v5x in detecting fixed prosthetic restoration in panoramic radiographs., Study Design: Descriptive study. Place and Duration of the Study: Department of Oral and Maxillofacial Radiology, Eskisehir Osmangazi University, Eskisehir, Turkiye from November 2022 to April 2023., Methodology: For the labelling of fixed prosthetic restorations, 8,000 panoramic radiographs were evaluated using the YOLO-v5x architecture. In creating the dataset for this study, fixed prosthetic restorations were categorised as dental implant, pontic, crown, and implant-supported crown on dental panoramic radiographs. The labelled images were then randomly split into three groups: 80% for training, 10% for validation, and 10% for testing. The labelled panoramic images constituted the model's training dataset, and leveraging the knowledge acquired during this learning stage, the model generated predictions in the testing phase., Results: The majority of labelling data were dedicated to crown restorations. The precision and sensitivity values of YOLOv5x were 0.99 and 0.98 for crowns, 0.98 and 0.99 for implants, 0.99 and 0.99 for pontics, and 0.99 and 0.99 for implant-supported crowns, respectively., Conclusion: The results obtained in this study demonstrate a satisfactory success rate of YOLO-v5x in detecting dental prosthetic restorations. The high precision and sensitivity of the model indicate its strong potential to enhance clinical professional performance and contribute to the development of more efficient dental health services., Key Words: Artificial intelligence, Dentistry, Dental prosthesis, Panoramic radiography.

Objective: This study aimed to assess the effectiveness of deep convolutional neural network (CNN) algorithms in detecting and segmentation of overhanging dental restorations in bitewing radiographs., Method and Materials: A total of 1160 anonymized bitewing radiographs were used to progress the Artificial Intelligence (AI) system) for the detection and segmentation of overhanging restorations. The data was then divided into three groups: 80% for training (930 images, 2399 labels), 10% for validation (115 images, 273 labels), and 10% for testing (115 images, 306 labels). A CNN model known as you only look once (YOLOv5) was trained to detect overhanging restorations in bitewing radiographs. After utilizing the remaining 115 radiographs to evaluate the efficacy of the proposed CNN model, the accuracy, sensitivity, precision, F1 score, and area under the receiver operating characteristic curve (AUC) were computed., Results: The model demonstrated a precision of 90.9%, a sensitivity of 85.3%, and an F1 score of 88.0%. Furthermore, the model achieved an AUC of 0.859 on the Receiver Operating Characteristic (ROC) curve. The mean average precision (mAP) at an intersection over a union (IoU) threshold of 0.5 was notably high at 0.87., Conclusion: The findings suggest that deep CNN algorithms are highly effective in the detection and diagnosis of overhanging dental restorations in bitewing radiographs. The high levels of precision, sensitivity, and F1 score, along with the significant AUC and mAP values, underscore the potential of these advanced deep learning techniques in revolutionizing dental diagnostic procedures., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Institute of Radiology and the International Association of Dentomaxillofacial Radiology.)

Objectives: In the interpretation of panoramic radiographs (PRs), the identification and numbering of teeth is an important part of the correct diagnosis. This study evaluates the effectiveness of YOLO-v5 in the automatic detection, segmentation, and numbering of deciduous and permanent teeth in mixed dentition pediatric patients based on PRs., Methods: A total of 3854 mixed pediatric patients PRs were labelled for deciduous and permanent teeth using the CranioCatch labeling program. The dataset was divided into three subsets: training (n = 3093, 80% of the total), validation (n = 387, 10% of the total) and test (n = 385, 10% of the total). An artificial intelligence (AI) algorithm using YOLO-v5 models were developed., Results: The sensitivity, precision, F-1 score, and mean average precision-0.5 (mAP-0.5) values were 0.99, 0.99, 0.99, and 0.98 respectively, to teeth detection. The sensitivity, precision, F-1 score, and mAP-0.5 values were 0.98, 0.98, 0.98, and 0.98, respectively, to teeth segmentation., Conclusions: YOLO-v5 based models can have the potential to detect and enable the accurate segmentation of deciduous and permanent teeth using PRs of pediatric patients with mixed dentition., (© 2024. The Author(s).)

Background: The purpose of this study was to investigate the morphology of maxillary first premolar mesial root concavity and to analyse its relation to periodontal bone loss (BL) using cone beam computed tomography (CBCT) and panoramic radiographs., Methods: The mesial root concavity of maxillary premolar teeth was analysed via CBCT. The sex and age of the patients, starting position and depth of the root concavity, apicocoronal length of the concavity on the crown or root starting from the cementoenamel junction (CEJ), total apicocoronal length of the concavity, amount of bone loss both in CBCT images and panoramic radiographs, location of the furcation, length of the buccal and palatinal roots, and buccopalatinal cervical root width were measured., Results: A total of 610 patients' CBCT images were examined, and 100 were included in the study. The total number of upper premolar teeth was 200. The patients were aged between 18 and 65 years, with a mean age of 45.21 ± 13.13 years. All the teeth in the study presented mesial root concavity (100%, n = 200). The starting point of concavity was mostly on the cervical third of the root (58.5%). The mean depth and buccolingual length measurements were 0.96 mm and 4.32 mm, respectively. Depth was significantly related to the amount of alveolar bone loss (F = 5.834, p = 0.001). The highest average concavity depth was 1.29 mm in the group with 50% bone loss. The data indicated a significant relationship between the location of the furcation and bone loss (X 2  = 25.215, p = 0.003). Bone loss exceeded 50% in 100% of patients in whom the furcation was in the cervical third and in only 9.5% of patients in whom the furcation was in the apical third (p = 0.003)., Conclusions: According to the results of this study, the depth of the mesial root concavity and the coronal position of the furcation may increase the amount of alveolar bone loss. Clinicians should be aware of these anatomical factors to ensure accurate treatment planning and successful patient management., (© 2024. The Author(s).)

Objectives: The purpose of this study was to evaluate the effectiveness of dental caries segmentation on the panoramic radiographs taken from children in primary dentition, mixed dentition, and permanent dentition with Artificial Intelligence (AI) models developed using the deep learning method., Methods: This study used 6075 panoramic radiographs taken from children aged between 4 and 14 to develop the AI model. The radiographs included in the study were divided into three groups: primary dentition (n: 1857), mixed dentition (n: 1406), and permanent dentition (n: 2812). The U-Net model implemented with PyTorch library was used for the segmentation of caries lesions. A confusion matrix was used to evaluate model performance., Results: In the primary dentition group, the sensitivity, precision, and F1 scores calculated using the confusion matrix were found to be 0.8525, 0.9128, and 0.8816, respectively. In the mixed dentition group, the sensitivity, precision, and F1 scores calculated using the confusion matrix were found to be 0.7377, 0.9192, and 0.8185, respectively. In the permanent dentition group, the sensitivity, precision, and F1 scores calculated using the confusion matrix were found to be 0.8271, 0.9125, and 0.8677, respectively. In the total group including primary, mixed, and permanent dentition, the sensitivity, precision, and F1 scores calculated using the confusion matrix were 0.8269, 0.9123, and 0.8675, respectively., Conclusions: Deep learning-based AI models are promising tools for the detection and diagnosis of caries in panoramic radiographs taken from children with different dentition.

Objectives: The study aims to develop an artificial intelligence (AI) model based on nnU-Net v2 for automatic maxillary sinus (MS) segmentation in cone beam computed tomography (CBCT) volumes and to evaluate the performance of this model., Methods: In 101 CBCT scans, MS were annotated using the CranioCatch labelling software (Eskisehir, Turkey) The dataset was divided into 3 parts: 80 CBCT scans for training the model, 11 CBCT scans for model validation, and 10 CBCT scans for testing the model. The model training was conducted using the nnU-Net v2 deep learning model with a learning rate of 0.00001 for 1000 epochs. The performance of the model to automatically segment the MS on CBCT scans was assessed by several parameters, including F1-score, accuracy, sensitivity, precision, area under curve (AUC), Dice coefficient (DC), 95% Hausdorff distance (95% HD), and Intersection over Union (IoU) values., Results: F1-score, accuracy, sensitivity, precision values were found to be 0.96, 0.99, 0.96, 0.96, respectively for the successful segmentation of maxillary sinus in CBCT images. AUC, DC, 95% HD, IoU values were 0.97, 0.96, 1.19, 0.93, respectively., Conclusions: Models based on nnU-Net v2 demonstrate the ability to segment the MS autonomously and accurately in CBCT images., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Institute of Radiology and the International Association of Dentomaxillofacial Radiology.)

Background: Deep learning model trained on a large image dataset, can be used to detect and discriminate targets with similar but not identical appearances. The aim of this study is to evaluate the post-training performance of the CNN-based YOLOv5x algorithm in the detection of white spot lesions in post-orthodontic oral photographs using the limited data available and to make a preliminary study for fully automated models that can be clinically integrated in the future., Methods: A total of 435 images in JPG format were uploaded into the CranioCatch labeling software and labeled white spot lesions. The labeled images were resized to 640 × 320 while maintaining their aspect ratio before model training. The labeled images were randomly divided into three groups (Training:349 images (1589 labels), Validation:43 images (181 labels), Test:43 images (215 labels)). YOLOv5x algorithm was used to perform deep learning. The segmentation performance of the tested model was visualized and analyzed using ROC analysis and a confusion matrix. True Positive (TP), False Positive (FP), and False Negative (FN) values were determined., Results: Among the test group images, there were 133 TPs, 36 FPs, and 82 FNs. The model's performance metrics include precision, recall, and F1 score values of detecting white spot lesions were 0.786, 0.618, and 0.692. The AUC value obtained from the ROC analysis was 0.712. The mAP value obtained from the Precision-Recall curve graph was 0.425., Conclusions: The model's accuracy and sensitivity in detecting white spot lesions remained lower than expected for practical application, but is a promising and acceptable detection rate compared to previous study. The current study provides a preliminary insight to further improved by increasing the dataset for training, and applying modifications to the deep learning algorithm., Clinical Revelance: Deep learning systems can help clinicians to distinguish white spot lesions that may be missed during visual inspection., (© 2024. The Author(s).)

Objectives: 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.

Objective: This study aimed to examine the morphological characteristics of the nasopharynx in unilateral Cleft lip/palate (CL/P) children and non-cleft children using cone beam computed tomography (CBCT)., Methods: A retrospective study consisted of 54 patients, of which 27 patients were unilateral CL/P, remaining 27 patients have no CL/P. Eustachian tubes orifice (ET), Rosenmuller fossa (RF) depth, presence of pharyngeal bursa (PB), the distance of posterior nasal spine (PNS)-pharynx posterior wall were quantitatively evaluated., Results: The main effect of the CL/P groups was found to be effective on RF depth-right ( p < 0.001) and RF depth-left ( p  < 0.001). The interaction effect of gender and CL/P groups was not influential on measurements. The cleft-side main effect was found to be effective on RF depth-left ( p  < 0.001) and RF depth-right ( p   =  0002). There was no statistically significant relationship between CL/P groups and the presence of bursa pharyngea., Conclusions: Because it is the most common site of nasopharyngeal carcinoma (NPC), the anatomy of the nasopharynx should be well known in the early diagnosis of NPC.

Objectives: 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., (© 2022. The Author(s) under exclusive licence to Japanese Society for Oral and Maxillofacial Radiology.)

Objectives: The aim of this study is to recommend an automatic caries detection and segmentation model based on the Convolutional Neural Network (CNN) algorithms in dental bitewing radiographs using VGG-16 and U-Net architecture and evaluate the clinical performance of the model comparing to human observer., Methods: A total of 621 anonymized bitewing radiographs were used to progress the Artificial Intelligence (AI) system (CranioCatch, Eskisehir, Turkey) for the detection and segmentation of caries lesions. The radiographs were obtained from the Radiology Archive of the Department of Oral and Maxillofacial Radiology of the Faculty of Dentistry of Ordu University. VGG-16 and U-Net implemented with PyTorch models were used for the detection and segmentation of caries lesions, respectively., Results: The sensitivity, precision, and F-measure rates for caries detection and caries segmentation were 0.84, 0.81; 0.84, 0.86; and 0.84, 0.84, respectively. Comparing to 5 different experienced observers and AI models on external radiographic dataset, AI models showed superiority to assistant specialists., Conclusion: CNN-based AI algorithms can have the potential to detect and segmentation of dental caries accurately and effectively in bitewing radiographs. AI algorithms based on the deep-learning method have the potential to assist clinicians in routine clinical practice for quickly and reliably detecting the tooth caries. The use of these algorithms in clinical practice can provide to important benefit to physicians as a clinical decision support system in dentistry., (© 2021. The Author(s) under exclusive licence to Japanese Society for Oral and Maxillofacial Radiology.)

Purpose: The purpose of this study is to explore sphenoid sinus variations in individuals with various sagittal skeletal anomalies using cone-beam computed tomography (CBCT)., Materials and Methods: We retrospectively analyzed sphenoid sinus pneumatization on CBCT images of 126 patients aged 18-86 years. The anteroposterior skeletal relationships of the maxilla and mandible were classified as skeletal class I, II or III using the A point-nasion-B point (ANB) angle measured in the sagittal plane. The extensions of the sphenoid sinus were evaluated on three planes including axial, sagittal and coronal sections., Results: The study population consisted of 84 females (66.7%) and 42 males (33.3%), including 52 (41.3%) class I, 38 (30.1%) class II, and 36 (28.6%) class III cases. The conchal type of sphenoid sinus was not encountered. Presellar sinuses were detected in only 3 (5.8%) class I cases. Incomplete sinuses were detected in 16 (30.8%) class I, 7 (18.4%) class II, and 15 (41.7%) class III cases. Complete sinuses were detected in 33 (63.4%) class I, 31 (81.6%) class II, and 21 (58.3%) class III cases. Lateral extensions were found in 103 (40.9%) of the 252 sinus walls: 33 (31.7%) in class I, 45 (59.2%) in class II, and 25 (34.7%) in class III sinuses., Conclusion: Regional sphenoid sinus anatomy can be carefully examined via CBCT. The sphenoid sinus pneumatization did not differ significantly in patients exhibiting different types of sagittal skeletal closure, with the exception of the lesser wing type., (Copyright © 2022 European Oral Research.)

Objectives: The goal of this study was to develop and evaluate the performance of a new deep-learning (DL) artificial intelligence (AI) model for diagnostic charting in panoramic radiography., Methods: One thousand eighty-four anonymous dental panoramic radiographs were labeled by two dento-maxillofacial radiologists for ten different dental situations: crown, pontic, root-canal treated tooth, implant, implant-supported crown, impacted tooth, residual root, filling, caries, and dental calculus. AI Model CranioCatch, developed in Eskişehir, Turkey and based on a deep CNN method, was proposed to be evaluated. A Faster R-CNN Inception v2 (COCO) model implemented with the TensorFlow library was used for model development. The assessment of AI model performance was evaluated with sensitivity, precision, and F1 scores., Results: When the performance of the proposed AI model for detecting dental conditions in panoramic radiographs was evaluated, the best sensitivity values were obtained from the crown, implant, and impacted tooth as 0.9674, 0.9615, and 0.9658, respectively. The worst sensitivity values were obtained from the pontic, caries, and dental calculus, as 0.7738, 0.3026, and 0.0934, respectively. The best precision values were obtained from pontic, implant, implant-supported crown as 0.8783, 0.9259, and 0.8947, respectively. The worst precision values were obtained from residual root, caries, and dental calculus, as 0.6764, 0.5096, and 0.1923, respectively. The most successful F1 Scores were obtained from the implant, crown, and implant-supported crown, as 0.9433, 0.9122, and 0.8947, respectively., Conclusion: The proposed AI model has promising results at detecting dental conditions in panoramic radiographs, except for caries and dental calculus. Thanks to the improvement of AI models in all areas of dental radiology, we predict that they will help physicians in panoramic diagnosis and treatment planning, as well as digital-based student education, especially during the pandemic period., (© 2021. The Author(s), under exclusive licence to Japanese Society for Oral and Maxillofacial Radiology and Springer Nature Singapore Pte Ltd.)

Telemedicine offers an excellent opportunity to provide continuing health care for those in need during local/global pandemics and disasters. It provides a safe and effective communication tool between health professionals and can be used as "forward triage" to manage medical/dental emergencies and to minimize the contact between the patients and clinicians during the coronavirus disease (COVID-19) pandemic. Patients with noncommunicable diseases, like cancer, diabetes, cardiovascular, or chronic respiratory diseases, may present with critical health problems due to less access to health care systems during global disasters; opportunities for screening oral mucosa might be significantly disrupted, leading to delayed diagnosis of malignant/potentially malignant lesions. Telemedicine and oral health care associated mobile applications should be implemented to provide equal access to care, to eliminate unnecessary visits to health centers, and to improve practical coordination between professionals and health facilities.

Objectives: The present study aimed to evaluate the performance of a Faster Region-based Convolutional Neural Network (R-CNN) algorithm for tooth detection and numbering on periapical images., Methods: The data sets of 1686 randomly selected periapical radiographs of patients were collected retrospectively. A pre-trained model (GoogLeNet Inception v3 CNN) was employed for pre-processing, and transfer learning techniques were applied for data set training. The algorithm consisted of: (1) the Jaw classification model, (2) Region detection models, and (3) the Final algorithm using all models. Finally, an analysis of the latest model has been integrated alongside the others. The sensitivity, precision, true-positive rate, and false-positive/negative rate were computed to analyze the performance of the algorithm using a confusion matrix., Results: An artificial intelligence algorithm (CranioCatch, Eskisehir-Turkey) was designed based on R-CNN inception architecture to automatically detect and number the teeth on periapical images. Of 864 teeth in 156 periapical radiographs, 668 were correctly numbered in the test data set. The F1 score, precision, and sensitivity were 0.8720, 0.7812, and 0.9867, respectively., Conclusion: The study demonstrated the potential accuracy and efficiency of the CNN algorithm for detecting and numbering teeth. The deep learning-based methods can help clinicians reduce workloads, improve dental records, and reduce turnaround time for urgent cases. This architecture might also contribute to forensic science.

The purpose of the paper was the assessment of the success of an artificial intelligence (AI) algorithm formed on a deep-convolutional neural network (D-CNN) model for the segmentation of apical lesions on dental panoramic radiographs. A total of 470 anonymized panoramic radiographs were used to progress the D-CNN AI model based on the U-Net algorithm (CranioCatch, Eskisehir, Turkey) for the segmentation of apical lesions. The radiographs were obtained from the Radiology Archive of the Department of Oral and Maxillofacial Radiology of the Faculty of Dentistry of Eskisehir Osmangazi University. A U-Net implemented with PyTorch model (version 1.4.0) was used for the segmentation of apical lesions. In the test data set, the AI model segmented 63 periapical lesions on 47 panoramic radiographs. The sensitivity, precision, and F1-score for segmentation of periapical lesions at 70% IoU values were 0.92, 0.84, and 0.88, respectively. AI systems have the potential to overcome clinical problems. AI may facilitate the assessment of periapical pathology based on panoramic radiographs., Competing Interests: The authors declare that there is no conflict of interest regarding the publication of this paper., (Copyright © 2022 Ibrahim S. Bayrakdar et al.)

Objective: The purpose of this study was to evaluate the root canal shaping abilities of different heat-treated NiTi engine--driven single files., Method and Materials: A total of 45 mandibular first molar teeth with a root canal curvature of between 25 and 35 degrees were selected for this in vitro study. The mesial roots were separated and scanned with micro-CT. The specimens were randomly divided into three groups. Root canal preparations were performed using HyFlex EDM OneFile in Group 1; OneCurve (25/06) in Group 2, and WaveOne Gold Primary in Group 3. Root canals were scanned again with micro-- CT after root canal preparation. Apical transportation value and centering ability ratio of files were evaluated using the preinstrumentation and postinstrumentation micro-CT images at 1, 2, 3, 4, and 7 mm. The data were statistically analyzed using the Kruskal-Wallis test. The Bonferroni-Dunn test was used for multiple comparisons. The level of statistical significance was set at P < .05., Results: There was no significant difference between the apical transportation values of experimental groups in mesiodistal direction (P > .05) and buccolingual direction (P > .05). The OneCurve file group showed better centering ability in the buccolingual direction than the WaveOne Gold file group at 4 mm (P = .048). The difference between the centering ability values of experimental groups was not significant at other levels (P > .05)., Conclusion: According to the results of this study, all tested single files caused apical transportation and showed similar centering ratio at most of the root sections.

Objective: The purpose of the study was to create an artificial intelligence (AI) system for detecting idiopathic osteosclerosis (IO) on panoramic radiographs for automatic, routine, and simple evaluations., Subject and Methods: In this study, a deep learning method was carried out with panoramic radiographs obtained from healthy patients. A total of 493 anonymized panoramic radiographs were used to develop the AI system (CranioCatch, Eskisehir, Turkey) for the detection of IOs. The panoramic radiographs were acquired from the radiology archives of the Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University. GoogLeNet Inception v2 model implemented with TensorFlow library was used for the detection of IOs. Confusion matrix was used to predict model achievements., Results: Fifty IOs were detected accurately by the AI model from the 52 test images which had 57 IOs. The sensitivity, precision, and F-measure values were 0.88, 0.83, and 0.86, respectively., Conclusion: Deep learning-based AI algorithm has the potential to detect IOs accurately on panoramic radiographs. AI systems may reduce the workload of dentists in terms of diagnostic efforts., (© 2022 The Author(s). Published by S. Karger AG, Basel.)

Background: The posterior wall of the nasopharynx is composed of loose connective tissue that includes many important anatomical structures. Various structures, such as the opening of the Eustachian tube (ET), the Rosenmüller fossa (RF), and the pharyngeal bursa (PB) are found here., Aim: To evaluate the nasopharynx posterior wall anatomic structures, including the Eustachian tube, Rosenmüller fossa, and pharyngeal bursa with cone-beam computed tomography., Materials and Methods: The depth, width, and length of the Eustachian tube, Rosenmüller fossa, and pharyngeal bursa were measured in 150 patients using axial-sagittal cone-beam computed tomography. The Eustachian tube and Rosenmüller fossa distance to the midsagittal plane, the coronal region passing through the posterior end of the nasal septum, the superior-inferior extremity of the recesses, and the nasal floor plane distance were measured. The relationship between Rosenmüller fossa types and other parameters were evaluated., Results: The incidence of right Rosenmüller fossa types 1, 2, and 3 were 16%, 18%, and 66%, respectively, and that of the left Rosenmüller fossa types 1, 2, and 3 were 16%, 19.3%, and 64.7%, respectively. The mean pharyngeal bursa width, length, and depth were 10.8, 5.7, and 4.0 mm, respectively; those of the Eustachian tube were 5.6, 7.1, and 7.3 m, respectively; those of the right Rosenmüller fossa were 4.0, 12.4, and 10.5 mm, respectively; and those of the left Rosenmüller fossa were 3.8, 12.5, and 10.9 mm, respectively., Conclusions: The posterior wall of the nasopharynx contains several important anatomical structures. Evaluation of these using cone-beam computed tomography has many clinical and radiological advantages. To understand and interpret the coincidental findings in CBCT, dental radiologists should have access to more detailed information concerning the anatomy of the nasopharynx., Competing Interests: None

Objective: This study evaluated the use of a deep-learning approach for automated detection and numbering of deciduous teeth in children as depicted on panoramic radiographs., Methods and Materials: An artificial intelligence (AI) algorithm (CranioCatch, Eskisehir-Turkey) using Faster R-CNN Inception v2 (COCO) models were developed to automatically detect and number deciduous teeth as seen on pediatric panoramic radiographs. The algorithm was trained and tested on a total of 421 panoramic images. System performance was assessed using a confusion matrix., Results: The AI system was successful in detecting and numbering the deciduous teeth of children as depicted on panoramic radiographs. The sensitivity and precision rates were high. The estimated sensitivity, precision, and F1 score were 0.9804, 0.9571, and 0.9686, respectively., Conclusion: Deep-learning-based AI models are a promising tool for the automated charting of panoramic dental radiographs from children. In addition to serving as a time-saving measure and an aid to clinicians, AI plays a valuable role in forensic identification.

Purpose: The aim of this study was to evaluate the diagnostic performance of artificial intelligence (AI) application evaluating of the impacted third molar teeth in Cone-beam Computed Tomography (CBCT) images., Material and Methods: In total, 130 third molar teeth (65 patients) were included in this retrospective study. Impaction detection, Impacted tooth numbers, root/canal numbers of teeth, relationship with adjacent anatomical structures (inferior alveolar canal and maxillary sinus) were compared between the human observer and AI application. Recorded parameters agreement between the human observer and AI application based on the deep-CNN system was evaluated using the Kappa analysis., Results: In total, 112 teeth (86.2%) were detected as impacted by AI. The number of roots was correctly determined in 99 teeth (78.6%) and the number of canals in 82 teeth (68.1%). There was a good agreement in the determination of the inferior alveolar canal in relation to the mandibular impacted third molars (kappa: 0.762) as well as the number of roots detection (kappa: 0.620). Similarly, there was an excellent agreement in relation to maxillary impacted third molar and the maxillary sinus (kappa: 0.860). For the maxillary molar canal number detection, a moderate agreement was found between the human observer and AI examinations (kappa: 0.424)., Conclusions: Artificial Intelligence (AI) application showed high accuracy values in the detection of impacted third molar teeth and their relationship to anatomical structures., (Copyright © 2020 Elsevier Masson SAS. All rights reserved.)

Competing Interests: Conflicts of Interest: None

Background: The aim of this study was to evaluate the success of the artificial intelligence (AI) system in implant planning using three-dimensional cone-beam computed tomography (CBCT) images., Methods: Seventy-five CBCT images were included in this study. In these images, bone height and thickness in 508 regions where implants were required were measured by a human observer with manual assessment method using InvivoDental 6.0 (Anatomage Inc. San Jose, CA, USA). Also, canals/sinuses/fossae associated with alveolar bones and missing tooth regions were detected. Following, all evaluations were repeated using the deep convolutional neural network (Diagnocat, Inc., San Francisco, USA) The jaws were separated as mandible/maxilla and each jaw was grouped as anterior/premolar/molar teeth region. The data obtained from manual assessment and AI methods were compared using Bland-Altman analysis and Wilcoxon signed rank test., Results: In the bone height measurements, there were no statistically significant differences between AI and manual measurements in the premolar region of mandible and the premolar and molar regions of the maxilla (p > 0.05). In the bone thickness measurements, there were statistically significant differences between AI and manual measurements in all regions of maxilla and mandible (p < 0.001). Also, the percentage of right detection was 72.2% for canals, 66.4% for sinuses/fossae and 95.3% for missing tooth regions., Conclusions: Development of AI systems and their using in future for implant planning will both facilitate the work of physicians and will be a support mechanism in implantology practice to physicians.

Objectives: Radiological examination has an important place in dental practice, and it is frequently used in intraoral imaging. The correct numbering of teeth on radiographs is a routine practice that takes time for the dentist. This study aimed to propose an automatic detection system for the numbering of teeth in bitewing images using a faster Region-based Convolutional Neural Networks (R-CNN) method., Methods: The study included 1125 bite-wing radiographs of patients who attended the Faculty of Dentistry of Ordu University from 2018 to 2019. A faster R-CNN an advanced object identification method was used to identify the teeth. The confusion matrix was used as a metric and to evaluate the success of the model., Results: The deep CNN system (CranioCatch, Eskisehir, Turkey) was used to detect and number teeth in bitewing radiographs. Of 715 teeth in 109 bite-wing images, 697 were correctly numbered in the test data set. The F1 score, precision and sensitivity were 0.9515, 0.9293 and 0.9748, respectively., Conclusions: A CNN approach for the analysis of bitewing images shows promise for detecting and numbering teeth. This method can save dentists time by automatically preparing dental charts.

Aim of the study: Ultrasonographic examination of intraosseous jaw pathologies may reveal interesting incidental, mobile hyperechoic particles ("snowflakes") in anechoic areas. Purpose of this study is to explain and discuss this snowing-like ultrasonographic feature of intraosseous jaw pathologies. Material and methods: This study included 113 patients admitted to our clinic for examination: 43 (38.05%) males and 70 (61.9%) females with a mean age of 34.9 ± 17.2 years (range: 6-72 years). A total of 120 intraosseous lesions were evaluated prior to surgery using ultrasonography; these included non-neoplastic, odontogenic, and non-odontogenic lesions. Results: In total, 5 (4.1%) of the 120 lesions exhibited snowing-like feature on ultrasonography, including 2 (1.6% of total) of 3 incisive canal cysts, 2 (1.6% of total) of 7 dentigerous cysts, and 1 (0.8% of total) of 19 odontogenic keratocysts. Conclusions: Snowflakes evident on ultrasonography of intraosseous jaw lesions may be specific to certain pathologies. Future studies correlating radiologic and pathologic features of intraosseous jaw lesions should focus on ultrasonographic snowing-like appearance in different types of lesions and explore why they occur., Competing Interests: Conflict of interest Authors do not report any financial or personal connections with other persons or organizations, which might negatively affect the contents of this publication and/or claim authorship rights to this publication., (© Polish Ultrasound Society.)

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.

Dental professionals have always been meticulous about infection control due to high risk of cross-contamination during dental procedures. Nevertheless, there is an urgent need to review and revise our current practice of infection control and develop more strict protocols that will prevent nosocomial spread of infection during COVID-19 outbreak and future pandemics. The risk of contamination is high during dental radiography if proper disinfection techniques are not applied. This document provides advice and guidance for infection control when practicing dental radiography during COVID-19 pandemic.

Objective  This study aimed to compare cranial base angulations in subjects with high-angle, low-angle, and normal-angle vertical growth patterns using cone beam computed tomography (CBCT). Design  This study is a retrospective clinical research. Settings  This study was carried out at the Dentistry Faculty of Eskisehir Osmangazi University. Participants  According to skeletal vertical face growth patterns, 78 subjects (48 females and 30 males, average age: 13.19 ± 1.73 years) were divided equally into three groups: high angle, low angle, and normal angle groups. Main Outcome Measures  Cephalometric images were derived from CBCT, and patients were classified according to the SN-GoGn angle (sella-nasion, gonion gnathion angle). Sagittal, axial, and coronal cranial base angulations were measured in three-dimensional (3D) CBCT images. Data were analyzed using the Kolmogorov-Smirnov normality, Kruskal-Wallis, and Mann-Whitney U statistical tests. Results  There were statistically significant differences between the low-angle and high-angle groups according to sagittal cranial base angulation parameters ( p  = 0.01). Conversely, there were no statistically significant differences between vertical facial growth patterns according to coronal and axial cranial angle variables ( p  > 0.05). Conclusion  According to the study results, there were no effects of cranial base angulations in two planes (coronal and axial) on different vertical skeletal growth patterns. In the sagittal cranial base angulation parameter, the high-angle group showed greater angulation values than the low-angle group. CBCT may be helpful for evaluating, diagnosing, and predicting 3D cranial base differences., Competing Interests: Conflict of Interest None declared., (Thieme. All rights reserved.)

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°; 24.7 ± 3.7 and 27.7 ± 4.3 mm; and 142.4 ± 7.8 and 136.3 ± 4.1°, respectively. All between-group differences were statistically significant (all P < 0.05). There were no significant between-gender differences in either group (all P > 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.

Aim: To verify the diagnostic performance of an artificial intelligence system based on the deep convolutional neural network method to detect periapical pathosis on cone-beam computed tomography (CBCT) images., Methodology: images of 153 periapical lesions obtained from 109 patients were included. The specific area of the jaw and teeth associated with the periapical lesions were then determined by a human observer. Lesion volumes were calculated using the manual segmentation methods using Fujifilm-Synapse 3D software (Fujifilm Medical Systems, Tokyo, Japan). The neural network was then used to determine (i) whether the lesion could be detected; (ii) if the lesion was detected, where it was localized (maxilla, mandible or specific tooth); and (iii) lesion volume. Manual segmentation and artificial intelligence (AI) (Diagnocat Inc., San Francisco, CA, USA) methods were compared using Wilcoxon signed rank test and Bland-Altman analysis., Results: The deep convolutional neural network system was successful in detecting teeth and numbering specific teeth. Only one tooth was incorrectly identified. The AI system was able to detect 142 of a total of 153 periapical lesions. The reliability of correctly detecting a periapical lesion was 92.8%. The deep convolutional neural network volumetric measurements of the lesions were similar to those with manual segmentation. There was no significant difference between the two measurement methods (P > 0.05)., Conclusions: Volume measurements performed by humans and by AI systems were comparable to each other. AI systems based on deep learning methods can be useful for detecting periapical pathosis on CBCT images for clinical application., (© 2020 International Endodontic Journal. Published by John Wiley & Sons Ltd.)

Purpose: The purpose of this study was to assess the three-dimensional morphometric features of the sella turcica using cone beam computed tomography (CBCT) in subjects with unilateral and bilateral maxillary impacted canines and normal controls., Methods: In this retrospective study, CBCT images captured with ultra-low dose protocol of 73 subjects (21 males, 52 females; mean age 20.01 ± 6.53 years) with unilateral or bilateral maxillary impacted canines (29 unilateral and 29 bilateral) and 15 controls were evaluated. Nineteen different measurements of the pituitary fossa were made on CBCT images. To evaluate the normality, the Kolmogorov-Smirnov test was used. The nonparametric statistical Kruskal-Wallis and Mann-Whitney U tests were applied to analyze the significant differences among and between the groups. Statistical significance was set at 5%., Results: No measurement differed significantly among the groups (all p > 0.05) other than the right sella length, which differed between the unilateral and bilateral test groups and the unilateral test group and controls (both p < 0.05). The bilateral test group and control group did not differ significantly, but both exhibited greater right sella length than did the unilateral test group (p > 0.05)., Conclusions: Other than the right sella length, there were no among-group differences in the mean pituitary fossa measurements of subjects with impacted unilateral and bilateral canines and normally erupted canines. The right sella length was lower in subjects with impacted unilateral canines than in those with bilateral impacted canines and normal controls.

The aim of this study was to determine the relationship between the presence of carotid artery calcification (CAC) and pulp stone (PS). A total of 60 chronic hemodialysis (HD) patients (30 CAC positive, 30 CAC negative) participated in this study. The mean age of patients was 54.7 ± 16.4 years, and 32 (53%) of them were male. CAC was defined as the presence of heterogeneous nodular opacities in the soft tissue in C3-C4 intervertebral area. Panoramic radiographs of the patients were evaluated for CAC and PS by two oral and maxillofacial radiologists. PS was evaluated in all healthy, decayed, and restored teeth except the third molar teeth, in the coronal, sagittal, and axial planes. The Statistical Package for the Social Sciences (version 20.0; SPSS, Inc., an IBM Company, Chicago, IL, USA) was used. A probability P <0.05 was considered statistically significant. The prevalence of PS in this study was 30% (18 patients) all group. A total of 1324 teeth were analyzed and PS was detected in 237 teeth (17.9%). The occurrence of PS in teeth in CAC-positive group (10 patients, 17.2% of 654 teeth) was similar to that in CAC-negative group (8 patient, 18.3% of 670 teeth). There was no statistical correlation between CAC and PS in chronic HD patients (P = 0.08). In the subgroup analysis, the presence of diabetes (P = 0.003), parathormone level (P = 0.02), calcium × phosphorus product (P = 0.04), and C-reactive protein levels (P = 0.002) were higher, and duration of HD (P = 0.03) was significantly longer in patients with CAC-positive and PS. In chronic HD patients, the presence of PS was not a strong predictor for the presence of CAC., Competing Interests: None

Objectives. The aim of the study is to detect the prevalence and the characteristics of infraorbital canal and Haller's cells on panoramic radiography of edentulous patients. Methods. The study group comprised 291 panoramic radiographs of edentulous patients. Radiographs were interpreted for the visibility and characteristics of infraorbital canal and Haller's cells. For classification of infraorbital canal, a method based on the image characteristics of the border of the canal (Types I, II, and III) was used. Haller's cells were grouped according to the number and the shape of loculations. Results. Infraorbital canal was observed in 246 (84.6%) radiographs. The most prevalent of the observed canals were Type III for both sides (39.9 % for right and 32.3% for left side). The visibility of Haller's cells was 23.7%. The frequencies of Haller's cells' visibility were approximately equal for both genders. There is no significant difference between genders for the visibility of infraorbital canal and Haller's cells. Conclusions. The surgeons, implantologists, and radiologists should take into consideration infraorbital canal and Haller's cell for planning implant surgery of maxillary anterior region and undefined orofacial pain for edentulous patients.

Objective: This study evaluated the prevalence and morphological characteristics of the superior semicircular canal (SSCC) in cleft lip and palate (CL/P) patients using cone beam computed tomography (CBCT)., Materials and Methods: CBCT images of 53 CL/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 (0.6-1.7 mm in thickness), a papyraceous pattern (<0.5 mm), a thick pattern (>1.8 mm), 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 (0.6-1.7 mm 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.

Objective: We assessed whether ultrasonography (US) can be used in combination with cone beam computed tomography (CBCT) to image intraosseous jaw lesions., Material and Methods: Using CBCT and US, we evaluated 123 lytic intraosseous jaw lesions diagnosed in 121 patients with guidance from the CBCT findings. The lesions were classified into two groups based on histopathological evaluation: (1) cysts and (2) tumors and tumor-like lesions. US and histopathological findings on the lesions of the two groups and their relationships with each other were also assessed. Results are reported as means ± standard errors, and p < 0.001 was accepted as indicating statistical significance., Result: In total, 123 lesions were evaluated; 74 (60.2%) were cysts and 49 (39.8%) were tumors or tumor-like lesions. The CBCT and US findings were compatible as far as dimensional measurements of the lesions in the three planes (p < 0.001). The US and histopathological findings on the content of the lesions correlated (p < 0.001)., Conclusion: CBCT provides useful information for diagnosing intraosseous jaw lesions. Because it offers no valid Hounsfield unit (HU) value, it does not differentiate between solid and cystic masses. Thus, US can be used with CBCT to image intraosseous jaw lesions caused by buccal cortical thinning or perforation., Clinical Relevance: US provides useful information about intraosseous jaw lesions and can be used with CBCT to image such lesions caused by buccal cortical thinning or perforation. Clinicians can take this information into consideration when evaluating intraosseous jaw pathology.

In the original version of this article, '123 lytic intraosseous jaw lesions diagnosed in 112 patients' was incorrectly presented as '123 lytic intraosseous jaw lesions diagnosed in 121 patients' and 'an average age of 31.7 ± 15.4 (range, 6-72)' was incorrectly presented as 'average age of 15.4 ± 31.7 (range, 6-72)'.

Ultrasonography (USG) is a diagnostic method that the ultrasonic image is created by ultrahigh-frequency sound waves, which have an acoustic frequency above the threshold of human hearing. Compared to other medical imaging methods, USG has several advantages of being real time, portable, inexpensive, radiation free, and noninvasive. In the medicine, most of the USG applications are transcutaneous. However, intraoral USG has been a relatively rare application, it has recently been drawing more interest. Intraoral USG is also used in dentistry for examining the salivary glands and ducts, as well as the mouth floor, the buccal, labial, and palatal mucosa, the tongue, periodontal tissues, and periapical lesions. The main purpose of this review is to provide detailed information about intraoral USG applications in dentistry., Competing Interests: There are no conflicts of interest.

Aim: This study aims to evaluate the root canal transportation, centering ability, and instrumentation times after root canal preparation using reciprocating motion at 300 rotations per minute (rpm) and 600 rpm., Materials and Methods: Twenty mesial root canals of mandibular first molars with curvature angles of 35°-70° and radii of 2-6 mm were included in the study. Root canal instrumentation was performed using R25 according to the manufacturer's instructions at 300 rpm or 600 rpm ( n = 10). Cone-beam computed tomography scanning was performed both pre- and post-instrumentation. Root canal transportation and the centering ratio were calculated for both the groups, and the data were analyzed using independent sample t -test for the instrumentation time, root canal transportation, and centering ratio at the 95% confidence level ( P = 0.05)., Results: At the three levels (3 mm, 5 mm, and 7 mm), there were no significant differences in centering ratio between the groups ( P > 0.05). At 3 mm, 600 rpm resulted in more transportation than 300 rpm. However, there were no significant differences in the root canal transportation between the groups at 5 mm and 7 mm levels ( P > 0.05)., Conclusion: At the 3 mm level, 600 rpm resulted in more transportation than 300 rpm. However, centering ratio was similar at both 600 rpm and 300 rpm., Competing Interests: There are no conflicts of interest.

Aim: The aim of this study is to evaluate the root canal transportation, centering ability, and instrumentation times with the ProTaper Gold (Dentsply Tulsa Dental, Tulsa, OK, USA), Reciproc (VDW, Munich, Germany), and ProTaper Universal (Dentsply Maillefer, Ballaigues, Switzerland) using cone-beam computed tomography (CBCT)., Materials and Methods: Thirty mesial root canals of mandibular first molars with curvature angles of 35°-70° and radii of 2-6 mm were included in the study. Root canal instrumentation was performed up to F2 or R25. The instrumentation times were recorded. CBCT scanning was performed both pre- and post-instrumentation. Root canal transportation and the centering ratio were calculated for groups, and the data were analyzed using a one-way ANOVA and least significant difference post hoc tests for the instrumentation time, root canal transportation, and centering ratio at the 95% confidence level ( P = 0.05)., Results: At 3, 5, and 7 mm levels, there was no significant difference in the root canal transportation and centering ratio among the groups ( P > 0.05). There were significant differences between the Reciproc and ProTaper Universal groups in the instrumentation times ( P < 0.05)., Conclusion: Root canal transportation and the centering ratio with the ProTaper Gold were similar to those obtained with the ProTaper Universal and Reciproc., Competing Interests: There are no conflicts of interest.

Objective: 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., (© 2016 S. Karger AG, Basel.)

Objective: 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.

Objectives: The purpose of this study was to evaluate fractal analysis as a tool to quantitatively determine the mandibular trabecular bone changes in patients with chronic renal failure (CRF)., Methods: In the present study, fractal analysis was performed using ImageJ (National Institutes of Health, Bethesda, MD) program with box-counting method over panoramic radiographs of 25 patients (14 females and 11 males) with CRF and 26 healthy individuals (14 females and 12 males) as a control group. The fractal dimension (FD) values of the patients and healthy individuals were compared. In addition, average biochemical parameters [parathyroid hormone (PTH), calcium (Ca), phosphorus (P), product of Ca and P levels (CaxP), alkaline phosphatase (ALP), vitamin D] of the patients with CRF, as measured during the 3 months before the panoramic radiographs, were compared with FD values., Results: According to the results, FD values of the patients with CRF were found to be statistically lower than the control group (p < 0.05). The average PTH levels of the patients with CRF were 416.16 ± 310.3 pg ml(-1); average Ca levels were 8.94 ± 1.2 mg dl(-1); average P levels were 5.76 ± 1.7 mg dl(-1); average CaxP values were 51.12 ± 15.03; average ALP levels were 83.44 ± 36.8 U l(-1); and the average vitamin D values were 19.43 ± 9.7 ng ml(-1). In addition, there was no significant correlation between FD values and the biochemical parameters of the patients, and there was no correlation between age, gender and FD., Conclusions: The FD values of the patients with CRF were lower than those of the controls. This finding suggests that FD analysis might be a promising simple and cost-effective tool for evaluating trabecular bone structure.

Impaction of a mandibular premolar is relatively uncommon. Ectopic placement is more unusual and there has been no discussion in the literature of an ectopic mandibular premolar in the coronoid process. In this case report, we present an impacted ectopic mandibular permanent premolar in the sigmoid notch (incisura mandibulae) region. Etiology of the tooth and treatment options are discussed and illustrated by Cone Beam Computed Tomography (CBCT) images.