OBJECTIVE: Oral diseases, specifically malignant lesions, are serious global health concerns requiring early diagnosis for effective treatment. In recent years, deep learning (DL) has emerged as a powerful tool for the automated detection and classif...
OBJECTIVES: Development of a prediction model using machine learning (ML) method for tumor progression in oral squamous cell carcinoma (OSCC) patients would provide risk estimation for individual patient outcomes.
As a relatively new tool, the use of artificial intelligence (AI) in medicine and dentistry has the potential to significantly transform the healthcare sector. AI has already demonstrated efficacy in medical diagnosis across several specialties, used...
OBJECTIVE: In this study, our aim was to develop and validate the effectiveness of diverse radiomic models for distinguishing between gnathic fibrous dysplasia (FD) and ossifying fibroma (OF) before surgery.
OBJECTIVES: To develop a dynamic self-attention and feature discrimination loss function (DSDF) model for identifying oral mucosal diseases presented to solve the problems of data imbalance, complex image background, and high similarity and differenc...