This study aimed at performing a systematic review of the literature on the application of
artificial intelligence (AI) in dental and maxillofacial cone beam computed tomography …

Objectives To conduct a scoping review focusing on artificial intelligence (AI) applications in
presurgical dental implant planning. Additionally, to assess the automation degree of …

Three-dimensional convolutional neural networks (3D CNN) of artificial intelligence (AI) are
potent in image processing and recognition using deep learning to perform generative and …

(1) Teeth, in humans, represent the most resilient tissues. However, exposure to
concentrated acids might lead to their dissolving, thus making human identification difficult …

Background There are different values reported about the performance of artificial
intelligence using cone-beam computed tomography (CBCT) for segmentation of oral and …

Purpose To evaluate the effects of exposure protocol, voxel sizes, and artifact removal
algorithms on the trueness of segmentation in various mandible regions using an artificial …

The purpose of this ambispective study was to investigate whether deep learning-based
automatic segmentation and landmark detection, the SkullEngine, could be used for …

Mandibular segmentation is an important step in gender identification and age estimation,
which aims to segment the mandible from intact and complete panoramic radiograph. One of …

Objectives The objective of this study is to develop a deep learning (DL) model for fast and
accurate mandibular canal (MC) segmentation on cone beam computed tomography …

Objectives This study introduced a three-dimensional (3D) surface-to-surface matching
technique to evaluate the mandibular symmetry of teenagers and adults with unilateral …