AIMC Topic: Cone-Beam Computed Tomography
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Zygomatic implant placement using a robot-assisted flapless protocol: proof of concept.
International journal of oral and maxillofacial surgery
Dec 12, 2022
Robotic assistance can help in physically guiding the drilling trajectory during zygomatic implant positioning. A new robot-assisted strategy for a flapless zygomatic implant placement protocol is reported here. In this protocol, a preoperative compu...
Deep learning-based motion compensation for four-dimensional cone-beam computed tomography (4D-CBCT) reconstruction.
Medical physics
Dec 3, 2022
BACKGROUND: Motion-compensated (MoCo) reconstruction shows great promise in improving four-dimensional cone-beam computed tomography (4D-CBCT) image quality. MoCo reconstruction for a 4D-CBCT could be more accurate using motion information at the CBC...
Effectiveness of cone-beam computed tomography-generated cephalograms using artificial intelligence cephalometric analysis.
Scientific reports
Nov 29, 2022
Lateral cephalograms and related analysis constitute representative methods for orthodontic treatment. However, since conventional cephalometric radiographs display a three-dimensional structure on a two-dimensional plane, inaccuracies may be produce...
Fully automated condyle segmentation using 3D convolutional neural networks.
Scientific reports
Nov 29, 2022
The aim of this study was to develop an auto-segmentation algorithm for mandibular condyle using the 3D U-Net and perform a stress test to determine the optimal dataset size for achieving clinically acceptable accuracy. 234 cone-beam computed tomogra...
Enhanced Tooth Region Detection Using Pretrained Deep Learning Models.
International journal of environmental research and public health
Nov 21, 2022
The rapid development of artificial intelligence (AI) has led to the emergence of many new technologies in the healthcare industry. In dentistry, the patient's panoramic radiographic or cone beam computed tomography (CBCT) images are used for implant...
Improving performance of deep learning models using 3.5D U-Net via majority voting for tooth segmentation on cone beam computed tomography.
Scientific reports
Nov 17, 2022
Deep learning allows automatic segmentation of teeth on cone beam computed tomography (CBCT). However, the segmentation performance of deep learning varies among different training strategies. Our aim was to propose a 3.5D U-Net to improve the perfor...
Clinical feasibility of deep learning-based automatic head CBCT image segmentation and landmark detection in computer-aided surgical simulation for orthognathic surgery.
International journal of oral and maxillofacial surgery
Nov 10, 2022
The purpose of this ambispective study was to investigate whether deep learning-based automatic segmentation and landmark detection, the SkullEngine, could be used for orthognathic surgical planning. Sixty-one sets of cone beam computed tomography (C...
Seq2Morph: A deep learning deformable image registration algorithm for longitudinal imaging studies and adaptive radiotherapy.
Medical physics
Nov 10, 2022
PURPOSE: To simultaneously register all the longitudinal images acquired in a radiotherapy course for analyzing patients' anatomy changes for adaptive radiotherapy (ART).
Comparison of deep learning segmentation and multigrader-annotated mandibular canals of multicenter CBCT scans.
Scientific reports
Nov 3, 2022
Deep learning approach has been demonstrated to automatically segment the bilateral mandibular canals from CBCT scans, yet systematic studies of its clinical and technical validation are scarce. To validate the mandibular canal localization accuracy ...
Artificial intelligence models for clinical usage in dentistry with a focus on dentomaxillofacial CBCT: a systematic review.
Oral radiology
Oct 21, 2022
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 (CBCT) and providing comprehensive descriptions of current technical inn...
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