OBJECTIVES: This study aims to explore and discuss recent advancements in tooth reconstruction utilizing deep learning (DL) techniques. A review on new DL methodologies in partial and full tooth reconstruction is conducted.
International journal of oral and maxillofacial surgery
38821731
The surgery-first approach (SFA) orthognathic surgery can be beneficial due to reduced overall treatment time and earlier profile improvement. The objective of this study was to utilize deep learning to predict the treatment modality of SFA or the or...
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: To assess the feasibility and accuracy of a new prototype robotic implant system for the placement of zygomatic implants in edentulous maxillary models.
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: Artificial intelligence (AI) is increasingly being integrated into intraoral scanners (IOS) to improve the quality of digital impressions. However, information on the accuracy of AI-assisted virtual models is limited. This study aimed to ...
IEEE transactions on neural networks and learning systems
38848227
Accurate teeth delineation on 3-D dental models is essential for individualized orthodontic treatment planning. Pioneering works like PointNet suggest a promising direction to conduct efficient and accurate 3-D dental model analyses in end-to-end lea...
OBJECTIVES: To validate the accuracy and reliability of a graph convolutional network (GCN)-based superimposition method of a maxillary digital dental model (MDM) by comparing it with manual superimposition and quantifying the clinical error from thi...
The application of deep learning using dental models is crucial for automated computer-aided treatment planning. However, developing highly accurate models requires a substantial amount of accurately labeled data. Obtaining this data is challenging, ...