AIMC Topic: Mandible

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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 ...
Tomography, X-Ray Computed Mandible Image Processing, Computer-Assisted Attention Humans Neural Networks, Computer
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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...
Humans Neural Networks, Computer Artificial Intelligence Cephalometry Reproducibility of Results Mandible
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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 Patient Care Planning Radiography, Dental Maxilla Jaw, Edentulous, Partially Bone Density Deep Learning Cone-Beam Computed Tomography Mandibular Canal Mandible Dental Implants Humans Neural Networks, Computer
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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...
Imaging, Three-Dimensional Mandible Cephalometry Humans Deep Learning Mandibular Advancement Orthognathic Surgical Procedures Lip Chin
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Comparison of machine learning methods for prediction of osteoradionecrosis incidence in patients with head and neck cancer.

The British journal of radiology Mar 18, 2021
OBJECTIVES: Mandible osteoradionecrosis (ORN) is one of the most severe toxicities in patients with head and neck cancer (HNC) undergoing radiotherapy (RT). The existing literature focuses on the correlation of mandible ORN and clinical and dosimetri...
Osteoradionecrosis Middle Aged Incidence Mandible Head and Neck Neoplasms Predictive Value of Tests Radiographic Image Interpretation, Computer-Assisted Machine Learning Reproducibility of Results Humans Tomography, X-Ray Computed Sensitivity and Specificity Female Male
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Deep learning based prediction of extraction difficulty for mandibular third molars.

Scientific reports Jan 21, 2021
This paper proposes a convolutional neural network (CNN)-based deep learning model for predicting the difficulty of extracting a mandibular third molar using a panoramic radiographic image. The applied dataset includes a total of 1053 mandibular thir...
Molar, Third Humans Mandible Deep Learning Tooth Extraction
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Comparison of different machine learning approaches to predict dental age using Demirjian's staging approach.

International journal of legal medicine Jan 7, 2021
CONTEXT: Dental age, one of the indicators of biological age, is inferred by radiological methods. Two of the most commonly used methods are using Demirjian's radiographic stages of permanent teeth excluding the third molar (Demirjian's and Willems' ...
Adolescent Child, Preschool Child Molar, Third Mandible Radiography, Panoramic Female Young Adult Male Humans Machine Learning Algorithms France Age Determination by Teeth Dentition, Permanent
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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.
Cone-Beam Computed Tomography Mandible Radiography, Panoramic Molar Tooth Root Humans Deep Learning Dental Pulp Cavity
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Trabeculae microstructure parameters serve as effective predictors for marginal bone loss of dental implant in the mandible.

Scientific reports Oct 28, 2020
Marginal bone loss (MBL) is one of the leading causes of dental implant failure. This study aimed to investigate the feasibility of machine learning (ML) algorithms based on trabeculae microstructure parameters to predict the occurrence of severe MBL...
Bone Resorption Mandible Logistic Models Support Vector Machine Dental Implants Cross-Sectional Studies Prosthesis Failure Humans Male Cancellous Bone Neural Networks, Computer Case-Control Studies Female
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Individual mandibular movement registration and reproduction using an optoeletronic jaw movement analyzer and a dedicated robot: a dental technique.

BMC oral health Oct 7, 2020
BACKGROUND: Fully adjustable articulators and pantographs record and reproduce individual mandibular movements. Although these instruments are accurate, they are operator-dependant and time-consuming. Pantographic recording is affected by inter and i...
Reproduction Movement Jaw Relation Record Mandible Robotics Dental Articulators
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