Computed tomography (CT) has been recognized as a robust and dependable technique for delineating osseous alterations and anomalies within hard tissues. The necessity for accurate diagnosis and management of patients with temporomandibular disorders in dental practices has increasingly come to the forefront. There is ongoing scholarly debate regarding the equivalence of diagnostic outcomes yielded by cone beam computed tomography (CBCT), which offers greater accessibility in dental settings than traditional CT, in identifying bony changes within the temporomandibular joint (TMJ). Our principal aim was to conduct a systematic review of studies that compare the efficacy of CT and CBCT in the detailed assessment of bone conditions affecting the TMJ. An electronic search was conducted across databases: PubMed, Medline, Web of Science, Cochrane and Scopus. Two independent reviewers screened titles and abstracts against predefined inclusion criteria. The included articles underwent rigorous critical appraisal, during which relevant data were extracted and systematically presented in a tabular format. This systematic review incorporates 5 studies published between 2006 and 2015. In 3 studies, CBCT demonstrated comparable outcomes to CT, while 2 investigations revealed significantly enhanced accuracy for CBCT compared to CT, with reported accuracies of 0.95 ± 0.04, 0.77 ± 0.17, and 89-91% for CBCT. The aggregated evidence from the included studies indicates that CBCT offers comparable or superior accuracy in detecting osseous changes within TMJ structures. Owing to its lower radiation exposure and increased accessibility, CBCT emerges as the preferred choice over conventional CT for evaluating bony structures of the TMJ.

Introduction: Developing new approaches by harnessing technological advancements alongside traditional dental education is crucial. The Department of Oral and Maxillofacial Radiology is particularly receptive to novel approaches and advancements both in education and in current practice. Staying close to new approaches and methods in educational materials and techniques is an inevitable necessity for keeping pace with the times. Objective and Method: The aim of this review is to discuss current studies regarding how new radiological approaches in dentistry, particularly artificial intelligence and its products, can provide differences and advantages in the field of education. Additionally, it aims to provide insights into future research and developments. It specifically aims to provide a brief summary of recent studies in this area. Results and Conclusions: In today's technology-driven era, gaining a contemporary perspective on educational materials and methods is highly important. With the changing world, it is necessary and inevitable to be open to new developments in education and to leverage the advancements in technology and software alongside traditional materials in radiology education. In light of the research conducted, there is a need for a greater variety and quantity of studies in the near future to further advance and make progress in this area.

Background/Objectives: Cervical lymph node metastasis (LNM) is the main prognostic factor in oral squamous cell carcinoma (OSCC) and guides the extent of neck treatment. This retrospective pilot study aimed to develop MRI-based radiomics machine learning models for the preoperative prediction of LNM in OSCC and to validate them internally at the patient level. Methods: Preoperative MRI of 74 patients (32 LNM-positive, 42 LNM-negative on histopathology) was analysed. In each patient the largest morphologically suspicious cervical lymph node was segmented volumetrically on axial contrast-enhanced T1-weighted images, and 1409 IBSI-compliant radiomic features were extracted. Performance was estimated by 10 repeats of 5-fold nested cross-validation with the patient as the unit of partitioning; standardisation, correlation pruning, LASSO selection, hyperparameter tuning and threshold choice were confined to the training folds. Six classifiers were compared. Results: LASSO retained a median of nine features (range 6–13). SVM performed best, with an AUC of 0.781 (95% CI 0.667–0.884), a sensitivity of 0.72, a specificity of 0.76 and a balanced accuracy of 0.74; XGBoost (AUC 0.774) and logistic regression (AUC 0.769) performed comparably. A slice-level analysis of the same data, in which slices from the same patient could fall into both training and test sets, gave an apparent AUC of 0.878 but a specificity of only 0.33. Conclusions: MRI radiomic features of cervical lymph nodes carry moderate patient-level information on LNM in OSCC, and slice-level designs can substantially misrepresent performance. External validation in larger multicentre cohorts is required before clinical use.

Background/Objectives: Zygomatic implants are commonly used to rehabilitate atrophic maxillae and maxillary defects, offering quicker prosthesis placement than bone grafting, but their placement requires technical precision due to the complexity of the zygomatic region. Trabecular bone quality is a key determinant of implant stability, yet data on the zygomatic bone’s microarchitecture remain limited. This study aimed to evaluate and compare trabecular bone microarchitectural features of the zygomatic bone with three reference sites (first molar region, mandibular condyle, and angle of mandible) using cone beam computed tomography (CBCT). Methods: This retrospective cross-sectional study screened 847 archived CBCT scans from the Ankara University Faculty of Dentistry, of which 99 systemically healthy patients aged 20–30 years were included. Trabecular bone microstructural parameters, including tissue volume, bone volume, percent bone volume, trabecular thickness, trabecular number, trabecular separation, and fractal dimension, were assessed using CTAn software at the zygomatic bone, first molar region, mandibular condyle, and angle of mandible. Results: The zygomatic bone exhibited significantly higher tissue and bone volumes than the other regions but lower percent bone volume and trabecular number; trabecular thickness and separation were greatest in the zygomatic bone, while fractal dimension values were lowest. When pooled across the four regions, significant right–left differences were also observed, with higher tissue volume and pore space on the right side. Conclusions: These findings provide baseline reference data on the zygomatic bone’s trabecular structure in a young, systemically healthy population. Further clinical studies in patient groups more representative of typical zygomatic implant candidates, including both older individuals with age-related maxillary atrophy and younger patients with trauma- or pathology-related bone loss, are needed to confirm the relevance of these findings to implant predictability and safety.