AIMC Topic: Cone-Beam Computed Tomography

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A deep learning approach for dental implant planning in cone-beam computed tomography images.

BMC medical imaging May 19, 2021
BACKGROUND: The aim of this study was to evaluate the success of the artificial intelligence (AI) system in implant planning using three-dimensional cone-beam computed tomography (CBCT) images.
Humans Neural Networks, Computer Mandibular Canal Mandible Dental Implants Deep Learning Cone-Beam Computed Tomography Bone Density Dental Implantation Jaw, Edentulous, Partially Maxilla Nasal Cavity Alveolar Process Patient Care Planning Radiography, Dental
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Deep-learning-based image registration and automatic segmentation of organs-at-risk in cone-beam CT scans from high-dose radiation treatment of pancreatic cancer.

Medical physics May 14, 2021
PURPOSE: Accurate deformable registration between computed tomography (CT) and cone-beam CT (CBCT) images of pancreatic cancer patients treated with high biologically effective radiation doses is essential to assess changes in organ-at-risk (OAR) loc...
Pancreatic Neoplasms Radiotherapy Planning, Computer-Assisted Cone-Beam Computed Tomography Image Processing, Computer-Assisted Retrospective Studies Humans Deep Learning
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Improving CBCT quality to CT level using deep learning with generative adversarial network.

Medical physics May 14, 2021
PURPOSE: To improve image quality and computed tomography (CT) number accuracy of daily cone beam CT (CBCT) through a deep learning methodology with generative adversarial network.
Humans Image Processing, Computer-Assisted Radiotherapy Planning, Computer-Assisted Spiral Cone-Beam Computed Tomography Deep Learning Cone-Beam Computed Tomography Tomography, X-Ray Computed
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Artificial Intelligence DENTOMO: Opportunities and Prospects for Interpretation of Cone Beam CT in Dentistry.

Bulletin of experimental biology and medicine Mar 31, 2021
The model of artificial intelligence DENTOMO, which allows automated deciphering the CT in maxillofacial area, was developed and implemented in practical dentistry. The presented model is based on two convolutional neural networks, includes a databas...
Artificial Intelligence Machine Learning Cone-Beam Computed Tomography Humans Neural Networks, Computer
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Multiclass CBCT Image Segmentation for Orthodontics with Deep Learning.

Journal of dental research Mar 30, 2021
Accurate segmentation of the jaw (i.e., mandible and maxilla) and the teeth in cone beam computed tomography (CBCT) scans is essential for orthodontic diagnosis and treatment planning. Although various (semi)automated methods have been proposed to se...
Humans Neural Networks, Computer Image Processing, Computer-Assisted Deep Learning Cone-Beam Computed Tomography Orthodontics Spiral Cone-Beam Computed Tomography
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3D morphometric quantification of maxillae and defects for patients with unilateral cleft palate via deep learning-based CBCT image auto-segmentation.

Orthodontics & craniofacial research Mar 25, 2021
OBJECTIVE: This study aimed to quantify the 3D asymmetry of the maxilla in patients with unilateral cleft lip and palate (UCP) and investigate the defect factors responsible for the variability of the maxilla on the cleft side using a deep-learning-b...
Cleft Palate Maxilla Spiral Cone-Beam Computed Tomography Cone-Beam Computed Tomography Cleft Lip Female Male Humans Adolescent Child Deep Learning
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A deep learning algorithm proposal to automatic pharyngeal airway detection and segmentation on CBCT images.

Orthodontics & craniofacial research Mar 8, 2021
OBJECTIVES: This study aims to evaluate an automatic segmentation algorithm for pharyngeal airway in cone-beam computed tomography (CBCT) images using a deep learning artificial intelligence (AI) system.
Spiral Cone-Beam Computed Tomography Cone-Beam Computed Tomography Image Processing, Computer-Assisted Humans Artificial Intelligence Retrospective Studies Deep Learning Algorithms
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Automatic Cephalometric Landmark Identification System Based on the Multi-Stage Convolutional Neural Networks with CBCT Combination Images.

Sensors (Basel, Switzerland) Jan 12, 2021
This study was designed to develop and verify a fully automated cephalometry landmark identification system, based on multi-stage convolutional neural networks (CNNs) architecture, using a combination dataset. In this research, we trained and tested ...
Cone-Beam Computed Tomography Databases, Factual Humans Cephalometry Retrospective Studies Neural Networks, Computer Anatomic Landmarks Imaging, Three-Dimensional Craniofacial Abnormalities Reproducibility of Results
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Artificial Intelligence for Fast and Accurate 3-Dimensional Tooth Segmentation on Cone-beam Computed Tomography.

Journal of endodontics Jan 9, 2021
INTRODUCTION: Tooth segmentation on cone-beam computed tomographic (CBCT) imaging is a labor-intensive task considering the limited contrast resolution and potential disturbance by various artifacts. Fully automated tooth segmentation cannot be achie...
Artifacts Tooth Root Tooth Artificial Intelligence Cone-Beam Computed Tomography
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Deep-learning for predicting C-shaped canals in mandibular second molars on panoramic radiographs.

Dento maxillo facial radiology Jan 6, 2021
OBJECTIVE: The aim of this study was to evaluate the use of a convolutional neural network (CNN) system for predicting C-shaped canals in mandibular second molars on panoramic radiographs.
Tooth Root Radiography, Panoramic Molar Dental Pulp Cavity Humans Cone-Beam Computed Tomography Deep Learning Mandible
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