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, Türkiye) 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.

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.

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.

In recent years, artificial intelligence (AI) has emerged as a powerful tool in medical imaging and in the analysis of complex bone pathologies such as cemento-osseous dysplasias. The aim of this study is to perform segmentation of cemento-osseous lesions using AI algorithms on cone beam computed tomography (CBCT) images and to evaluate the diagnostic performance of a diagnostic AI model designed for the diagnosis of cemento-osseous dysplasias. In this study, cone beam computed tomography (CBCT) images taken for various reasons in radiology archive Department of Oral and Maxillofacial Radiology were retrospectively reviewed. As a result of radiographic evaluation, images recorded in the archive with at diagnosis of cemento-osseous dysplasias were determined. Fifty DICOM images were uploaded to the 3D slicer software, and cemento-osseous dysplasias were polygonally labeled and saved in Neuroimaging Informatics Technology Initiative (NIfTI) format. The nnU-Net v2-based automated algorithm for lesion segmentation was developed using the CranioCatch (CranioCatch, Eskişehir) software program using the PyTorch library in the Python framework (v3.6.1; Python Software Foundation). 80% of the data was used for training, 10% for validation and 10% for testing. The results were evaluated according to the criteria of precision, sensitivity, Dice Coefficient, Jaccard Index. The precision, sensitivity, Dice Coefficient and Jaccard Index for the segmentation of cemento-osseous dysplasias were 0.805, 0.889, 0.839, and 0.730, respectively. The model we used achieved successful results in cemento-osseous dysplasias segments. The results of this planned study are promising in terms of providing a guidance for physicians in diagnosis. Automated segmentation of cemento-osseous lesions, where radiological images play a critical role in both diagnosis and follow-up, has the potential to enable precise and consistent definition of lesion boundaries and standardize the follow-up process, enabling more reliable data for long-term studies.