CONTEXT: Dental age, one of the indicators of biological age, is inferred by radiological methods. Two of the most commonly used methods are using Demirjian's radiographic stages of permanent teeth excluding the third molar (Demirjian's and Willems' ...
American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics
33795092
INTRODUCTION: This study aimed to evaluate the efficiency of a newly constructed computer-based decision support system (DSS) on the basis of artificial intelligence technology and designed to plan treatment for patients with a deep overbite.
OBJECTIVES: This study developed and validated a deep learning-based method to automatically segment and number teeth in panoramic radiographs across primary, mixed, and permanent dentitions.
OBJECTIVE: To introduce a novel approach for predicting the personalized probability of success of DPC treatment in carious mature permanent teeth using explainable machine learning (ML) models.
OBJECTIVE: To establish a high-precision, automated model using deep learning for the fine classification and three-dimensional (3D) segmentation of mixed dentition in cone-beam computed tomography (CBCT) images.
BACKGROUND: While artificial intelligence-driven approaches have shown great promise in dental diagnosis and treatment planning, most research focuses on dental radiographs. Only three studies have explored automated tooth numbering in oral photograp...