OBJECTIVES: This clinical follow-up evaluated the long-term outcome of full-mouth rehabilitations with adhesively bonded all-ceramic restorations in patients suffering from amelogenesis imperfecta (AI) or affected by extensive tooth wear including a ...
IEEE transactions on visualization and computer graphics
29994311
In this paper, we present a novel approach for 3D dental model segmentation via deep Convolutional Neural Networks (CNNs). Traditional geometry-based methods tend to receive undesirable results due to the complex appearance of human teeth (e.g., miss...
Forensic Odontology deals with identifying humans based on their dental traits because of their robust nature. Classical methods of human identification require more manual effort and are difficult to use for large number of Images. A Novel way of au...
In this paper, a new powerful deep learning framework, named as DENTECT, is developed in order to instantly detect five different dental treatment approaches and simultaneously number the dentition based on the FDI notation on panoramic X-ray images....
The aim of this study was to propose a novel method to identify individuals by recognizing dentition change, along with human identification process using deep learning. Recent and past images of adults aged 20-49 years with more than two dental pano...
OBJECTIVES: This study aimed to train a 3D U-Net convolutional neural network (CNN) for mandible and lower dentition segmentation from cone-beam computed tomography (CBCT) scans.
Journal of the World federation of orthodontists
39489636
INTRODUCTION: Advancements in technology have led to the adoption of digital workflows in dentistry, which require the segmentation of regions of interest from cone-beam computed tomography (CBCT) scans. These segmentations assist in diagnosis, treat...