Deep learning and diagnostic applications in oral and dental health have received significant attention recently. In this review, studies applying deep learning to diagnose anomalies and diseases in dental image material were systematically compiled, and their datasets, methodologies, test processes, explainable artificial intelligence methods, and findings were analyzed. Tests and results in studies involving human-artificial intelligence comparisons are discussed in detail to draw attention to the clinical importance of deep learning.
View Article and Find Full Text PDFPurpose: In this study, the required dose rates for optimal treatment of tumoral tissues when using proton therapy in the treatment of defective tumours seen in mandibles has been calculated. We aimed to protect the surrounding soft and hard tissues from unnecessary radiation as well as to prevent complications of radiation. Bragg curves of therapeutic energized protons for two different mandible (molar and premolar) plate phantoms were computed and compared with similar calculations in the literature.
View Article and Find Full Text PDFObjective: This study aimed to compare the Dental Discomfort Questionnaire (DDQ) scores in children with and without intellectual disability (ID) and to measure correlation between the total DDQ and the Decayed, Missing, and Filled Teeth (DMFT/dmft) scores, as well as the condition of the tooth causing pain.
Method: This cross-sectional study included 81 children with normal intellectual development who attended the Departments of Pediatric Dentistry at two Turkish Universities and 80 children with different levels of intellectual disability who reported dental pain in special education centers. The 12-question DDQ (Turkish version) was applied to the parents of the patients with their consent.