Beta-thalassemia major is an inherited disorder that can cause bone deformity and loss of bone mineral density. The objective of this study is to evaluate the cortical and trabecular mandibular bone morphology of children and adolescents who have beta-thalassemia major (ß-TM) using a fractal dimension (FD) analysis and different panoramic radiomorphometric indices with digital panoramic radiographic images (DPRIs). The study included 80 patients (with 40 patients each of ß-TM and control). The mandibular cortical width (MCW), panoramic mandibular index (PMI), mandibular cortical index (MCI), and simple visual estimation (SVE) were evaluated, and an FD analysis of five regions of interest (ROIs) (ROI 1: in basal cortical bone; ROI 2: in premolar region; ROI 3: in molar region; ROI 4: in angulus mandible and ROI 5: in condyle region) was obtained in all DPRIs. Quantitative variables were analyzed using the student’s t-test , Kruskal–Wallis and Mann-Whitney U tests. When the ß-TM groups were compared with controls, there were no statistically significant differences found in the mean FD values, the ROIs of the trabecular bone, or the SVE. There was a significant correlation in the mean MCW, PMI, ROI of cortical bone (ROI 1), and MCI between ß-TM and control groups (p < 0.001, p < 0.001, p = 0.047, and p = 0.046, respectively). The mean MCW values correlated with the SVE in both the ß-TM and control groups (p = 0.031 and p < 0.001, respectively). While the mean MCW values correlated with the MCI (p = 0.04) in the control group, the mean MCW values were not correlated with the MCI (p = 0.493) in ß-TM group. The current study revealed lower MCW and PMI values in the ß-TM group. While the mean FD values of trabecular bone is similar to the control groups, the mean FD value is lower in cortical bone in the ß-TM group. MCW, PMI, FD of cortical bone and MCI may be key indicators in individuals with beta-thalassemia major. Key words:Beta-thalassemia major, fractals, panoramic radiography.

The aim of this systematic review is to compare the efficacy of convolutional neural networks (CNN) and Vision Transformers (ViT) in the field of dental imaging, in order to examine in depth the potential, advantages, and limitations of both models in this domain. The search strings used in the study were “((“Vision Transformer” OR ViT OR “Transformer architecture”) AND (“Convolutional Neural Network” OR CNN OR ConvNet) AND (Dental OR Dentistry OR “Maxillofacial” OR “Oral Radiology”) AND (Image OR Imaging OR Radiograph))”. The search was conducted in January 2025. Two investigators independently evaluated the full texts of all eligible articles and excluded those that did not meet the inclusion/exclusion criteria. Of 2596 articles, 21 met the inclusion criteria. Depending on the task category, of the 21 studies that were reviewed, 14 (66.7%) utilized classification, while 7 (33.3%) utilized segmentation. Panoramic radiography is the most commonly used imaging modality (52.3%) and the ViT-based model was observed to have the highest performance (58%). ViT-based deep learning models tend to exhibit higher performance in many dental image analysis scenarios compared to traditional convolutional neural networks. However, in practice CNN and ViT approaches can be used in a complementary manner.

Cancer remains a significant global health concern, with increasing genetic and metabolic irregularities linked to its onset. Among various forms of cancer, skin cancer, including squamous cell carcinoma, basal cell carcinoma, and melanoma, is on the rise worldwide, often triggered by ultraviolet (UV) radiation. The propensity of skin cancer to metastasize highlights the importance of early detection for successful treatment. This narrative review explores the evolving role of artificial intelligence (AI) in diagnosing head and neck skin cancers from both radiological and pathological perspectives. In the past two decades, AI has made remarkable progress in skin cancer research, driven by advances in computational capabilities, digitalization of medical images, and radiomics data. AI has shown significant promise in image-based diagnosis across various medical domains. In dermatology, AI has played a pivotal role in refining diagnostic and treatment strategies, including genomic risk assessment. This technology offers substantial potential to aid primary clinicians in improving patient outcomes. Studies have demonstrated AI’s effectiveness in identifying skin lesions, categorizing them, and assessing their malignancy, contributing to earlier interventions and better prognosis. The rising incidence and mortality rates of skin cancer, coupled with the high cost of treatment, emphasize the need for early diagnosis. Further research and integration of AI into clinical practice are warranted to maximize its benefits in skin cancer diagnosis and treatment.

The objective of the research was to analyze the differences in root structure found in mandibular molars through cone beam computed tomography (CBCT) and to highlight their morphological features. This study retrospectively examined CBCT images of 200 patients, encompassing a total of 496 molars in the study group. The frequency of radix entomolaris (RE), radix paramolaris (RP), and C-shaped canals was assessed based on molar type, side, and gender. The data are presented as frequencies and percentages, and the Chi-square test was employed to analyze potential differences between groups. A significance level of p < 0.05 was set for all tests. The normality of the data was assessed using the Shapiro-Wilk test. The study included 91 males (47%) and 109 females (53%), with a mean age of 32.49 ± 14.29 years. The frequency of RE was 4.2% (n = 21), RP was 0.8% (n = 4) and C-shaped canal was 7.1% (n = 35) in 496 molars (first molars n = 247, second molars n = 249). RE, RP and C-shaped canal were found in 3.4%, 0.2% and 0.2% of the mandibular first molars, respectively; RE, RP and C-shaped canal were found in 0.8%, 0.6% and 6.8% of the second molars, respectively. There was no statistically significant difference between the root variations examined and gender and lateralisation (p > 0.05). Identifying anatomical variations in mandibular molars holds clinical importance, as it can improve endodontic treatment outcomes and provide insight into existing failures. Early detection of C-shaped canals and additional root structures can provide more effective cleaning and disinfection processes by more customizing treatment plans.