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: Artificial Intelligence (AI) in dental diagnostics is evolving, offering innovative approaches for conducting cephalometric analysis with less manual input and overcoming the limitations of traditional imaging methods. To enhance the diag...
OBJECTIVES: To construct a diagnostic model for mixed dentition using a multistage deep-learning network to predict potential ectopic eruption in permanent teeth by integrating dentition segmentation into the process of automatic classification of de...
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.
OBJECTIVES: This systematic review and meta-analysis aimed to investigate the diagnostic accuracy of Artificial Intelligence (AI) for approximal carious lesions on bitewing radiographs.
OBJECTIVES: This study aimed to systematically categorize the available literature and offer a comprehensive overview of artificial neural network (ANN) prediction models in prosthodontics. Specifically, the present research introduces a systematic a...
OBJECTIVES: To (1) construct a virtual patient (VP) using facial scan, intraoral scan, and low-dose computed tomography scab based on an Artificial intelligence (AI)-approach, (2) quantitatively compare it with AI-refined and semi-automatic registrat...
OBJECTIVES: This study aimed to develop and validate a robotic system capable of performing accurate and minimally invasive jawbone milling procedures in oral and maxillofacial surgery.
OBJECTIVES: Artificial intelligence (AI) could be used as an automatically diagnosis method for dental disease due to its accuracy and efficiency. This research proposed a novel convolutional neural network (CNN)-based deep learning (DL) ensemble mod...
OBJECTIVES: This study aimed to compare the design outcomes of anterior crowns generated using deep learning (DL)-based software with those fabricated by a technician using conventional dental computer-assisted design (CAD) software without DL suppor...