AIM: To develop and validate models based on logistic regression and artificial intelligence for prognostic prediction of molar survival in periodontally affected patients.
OBJECTIVES: Multiple generations of medical robots have revolutionized surgery. Their application to dental implants is still in its infancy. Co-operating robots (cobots) have great potential to improve the accuracy of implant placement, overcoming t...
OBJECTIVES: This study aimed to identify predictors associated with the tooth loss phenotype in a large periodontitis patient cohort in the university setting.
The Journal of clinical pediatric dentistry
38755985
Early tooth loss in pediatric patients can lead to various complications, making quick and accurate diagnosis essential. This study aimed to develop a novel deep learning model for classification of missing teeth on panoramic radiographs in pediatric...
Recent studies have shown that dental implants have high long-term survival rates, indicating their effectiveness compared to other treatments. However, there is still a concern regarding treatment failure. Deep learning methods, specifically U-Net m...
AIM: The aim of this analysis was to compare a clinical periodontal prognostic system and a developed and externally validated artificial intelligence (AI)-based model for the prediction of tooth loss in periodontitis patients under supportive period...
This study, for the first time, explores the integration of data science and machine learning for the classification and prediction of coronary artery calcium (CAC) scores. It focuses on tooth loss and patient characteristics as key input features to...
OBJECTIVES: This feasibility study investigates the application of artificial intelligence (AI) models, specifically transformer-based (TM) and diffusion-based (DM) models, for the reconstruction of single and multiple missing teeth.