AIMC Topic: Mandible

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Mandibular shape prediction model using machine learning techniques.

Clinical oral investigations Jan 8, 2022
OBJECTIVE: To create a mandibular shape prediction model using machine learning techniques and geometric morphometrics.
Machine Learning Mandible Cephalometry
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Fully automatic segmentation of the mandible based on convolutional neural networks (CNNs).

Orthodontics & craniofacial research Dec 1, 2021
OBJECTIVES: To evaluate the accuracy of automatic deep learning-based method for fully automatic segmentation of the mandible from CBCTs.
Reproducibility of Results Young Adult Adult Artificial Intelligence Female Male Mandible Image Processing, Computer-Assisted Humans Neural Networks, Computer
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A Deep Learning Approach to Segment and Classify C-Shaped Canal Morphologies in Mandibular Second Molars Using Cone-beam Computed Tomography.

Journal of endodontics Sep 24, 2021
INTRODUCTION: The identification of C-shaped root canal anatomy on radiographic images affects clinical decision making and treatment. The aims of this study were to develop a deep learning (DL) model to classify C-shaped canal anatomy in mandibular ...
Cone-Beam Computed Tomography Mandible Molar Tooth Root Deep Learning Dental Pulp Cavity
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Automated description of the mandible shape by deep learning.

International journal of computer assisted radiology and surgery Aug 27, 2021
PURPOSE: The shape of the mandible has been analyzed in a variety of fields, whether to diagnose conditions like osteoporosis or osteomyelitis, in forensics, to estimate biological information such as age, gender, and race or in orthognathic surgery....
Neural Networks, Computer Infant Deep Learning Humans Mandible Radiography, Panoramic Orthognathic Surgical Procedures
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Layered deep learning for automatic mandibular segmentation in cone-beam computed tomography.

Journal of dentistry Aug 20, 2021
OBJECTIVE: To develop and validate a layered deep learning algorithm which automatically creates three-dimensional (3D) surface models of the human mandible out of cone-beam computed tomography (CBCT) imaging.
Humans Artificial Intelligence Deep Learning Image Processing, Computer-Assisted Cone-Beam Computed Tomography Mandible
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Machine learning to predict distal caries in mandibular second molars associated with impacted third molars.

Scientific reports Jul 29, 2021
Impacted mandibular third molars (M3M) are associated with the occurrence of distal caries on the adjacent mandibular second molars (DCM2M). In this study, we aimed to develop and validate five machine learning (ML) models designed to predict the occ...
Data Accuracy Tooth, Impacted Tooth Cervix Dental Caries Dental Caries Susceptibility Humans Machine Learning Adult Sensitivity and Specificity Cross-Sectional Studies Male Molar, Third Female Retrospective Studies Mandible Risk Factors Clinical Decision-Making Young Adult
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Automatic mandible segmentation from CT image using 3D fully convolutional neural network based on DenseASPP and attention gates.

International journal of computer assisted radiology and surgery Jul 21, 2021
PURPOSE: In cranio-maxillofacial surgery, it is of great clinical significance to segment mandible accurately and automatically from CT images. However, the connected region and blurred boundary in teeth and condyles make the process challenging. At ...
Attention Humans Neural Networks, Computer Tomography, X-Ray Computed Mandible Image Processing, Computer-Assisted
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Comparison of cephalometric measurements between conventional and automatic cephalometric analysis using convolutional neural network.

Progress in orthodontics May 31, 2021
OBJECTIVE: The rapid development of artificial intelligence technologies for medical imaging has recently enabled automatic identification of anatomical landmarks on radiographs. The purpose of this study was to compare the results of an automatic ce...
Reproducibility of Results Humans Mandible Neural Networks, Computer Cephalometry Artificial Intelligence
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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.
Nasal Cavity Alveolar Process Dental Implantation Jaw, Edentulous, Partially Patient Care Planning Radiography, Dental Maxilla Mandible Dental Implants Deep Learning Humans Cone-Beam Computed Tomography Neural Networks, Computer Mandibular Canal Bone Density
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Three-dimensional virtual planning in mandibular advancement surgery: Soft tissue prediction based on deep learning.

Journal of cranio-maxillo-facial surgery : official publication of the European Association for Cranio-Maxillo-Facial Surgery Apr 21, 2021
The study aimed at developing a deep-learning (DL)-based algorithm to predict the virtual soft tissue profile after mandibular advancement surgery, and to compare its accuracy with the mass tensor model (MTM). Subjects who underwent mandibular advanc...
Orthognathic Surgical Procedures Imaging, Three-Dimensional Humans Lip Chin Mandible Deep Learning Mandibular Advancement Cephalometry
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