BACKGROUND: The purpose of the current study was to report on the clinical presentation and radiologic features of 155 cases of ameloblastoma (AB), representing a detailed, large, single-centre radiologic study.
METHODS: Histologically confirmed cases were reviewed over 11 years. Demographic and clinical data were retrieved from the patient\'s records. Radiologic information was analysed from available radiographs. The radiologic features of ABs were assessed according to the mean age of presentation and the mean duration of the lesion. The distinguishing radiologic features between adults/children and sex were also evaluated.
RESULTS: A statistically significant correlation existed between loss of border demarcation and advanced mean age. Multilocular lesions were markedly more common in adults compared to children. Multilocular ABs were associated with increased lesion duration and advanced mean age. Radiologic signs of reactive bony changes associated with the tumour presented at the highest mean duration of all bony effects. Bony expansion and cortical destruction were statistically correlated with lesion duration. Tooth impaction was more common in children. Some mandibular lesions reached a significant size, resulting in impingement of the maxillary sinus, zygoma, orbit and pterygoid plates.
CONCLUSIONS: Due to unfortunate healthcare access constraints, ABs grow to significant sizes and exhibit features not often reported in the literature. The findings of this analysis highlighted the radiologic features of ABs expressed through the mean age and duration of the lesion. This emphasises the significance of timely management of these lesions.

Recent advancements in deep learning and artificial intelligence (AI) have profoundly impacted various fields, including diagnostic imaging. Integrating AI technologies such as deep learning and convolutional neural networks has the potential to drastically improve diagnostic methods in the field of dentistry and maxillofacial radiography. A systematic study that adhered to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standards was carried out to examine the efficacy and uses of AI in dentistry and maxillofacial radiography. Incorporating cohort studies, case-control studies, and randomized clinical trials, the study used an interdisciplinary methodology. A thorough search spanning peer-reviewed research papers from 2009 to 2023 was done in databases including MEDLINE/PubMed and EMBASE. The inclusion criteria were original clinical research in English that employed AI models to recognize anatomical components in oral and maxillofacial pictures, identify anomalies, and diagnose disorders. The study looked at numerous research that used cutting-edge technology to show how accurate and dependable dental imaging is. Among the tasks covered by these investigations were age estimation, periapical lesion detection, segmentation of maxillary structures, assessment of dentofacial abnormalities, and segmentation of the mandibular canal. The study revealed important developments in the precise definition of anatomical structures and the identification of diseases. The use of AI technology in dental imaging marks a revolutionary development that will usher in a time of unmatched accuracy and effectiveness. These technologies have not only improved diagnostic accuracy and enabled early disease detection but have also streamlined intricate procedures, significantly enhancing patient outcomes. The symbiotic collaboration between human expertise and machine intelligence promises a future of more sophisticated and empathetic oral healthcare.

OBJECTIVE: To investigate the prevalence of incidental findings and need for further dental treatment and analyse the influence of size of field-of-view (FOV) and age in cone beam computed tomography (CBCT) for pre-implant planning.
METHODS: 404 CBCT scans were examined retrospectively for incidental findings and need for further dental treatment. Incidental finding-frequencies and need for further treatment were assessed for different age (< 40 years, 40-60 years, > 60 years) and FOV groups (small, medium, large). Intraexaminer and interexaminer agreements were evaluated.
RESULTS: In 82% of the scans at least one incidental finding was found, with a total of 766 overall. More incidental findings were found in scans with large FOV (98% vs. 72%, OR = 22.39 large vs. small FOV, p < 0.0001) and in scans of patients > 60 years (OR = 5.37 patient\'s age > 60 years vs. < 40 years, p = 0.0003). Further dental treatment due to incidental findings was needed in 31%. Scans with large FOV were more likely to entail further treatment (OR = 3.55 large vs. small FOV, p < 0.0001). Partial edentulism and large FOV were identified as risk factors for further treatment (p = 0.0003 and p < 0.0001). Further referral of the patient based on incidental findings was judged as indicated in 5%. Intra- and inter-examiner agreements were excellent (kappa = 0.944/0.805).
CONCLUSIONS: A considerable number of incidental findings with need for further dental treatment was found in partially edentulous patients and in patients > 60 years. In pre-implant planning of elderly patients, the selection of large FOV CBCT scans, including dentoalveolar regions not X-rayed recently, help to detect therapeutically relevant incidental findings.