AIMC Topic: Mandible

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Development and Validation of a Visually Explainable Deep Learning Model for Classification of C-shaped Canals of the Mandibular Second Molars in Periapical and Panoramic Dental Radiographs.

Journal of endodontics Apr 12, 2022
INTRODUCTION: The purpose of this study was to develop and validate a visually explainable deep learning model for the classification of C-shaped canals of the mandibular second molars in dental radiographs.
Humans Mandible Deep Learning Molar Tooth Root Dental Pulp Cavity Cone-Beam Computed Tomography
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Artificial intelligence in positioning between mandibular third molar and inferior alveolar nerve on panoramic radiography.

Scientific reports Feb 14, 2022
Determining the exact positional relationship between mandibular third molar (M3) and inferior alveolar nerve (IAN) is important for surgical extractions. Panoramic radiography is the most common dental imaging test. The purposes of this study were t...
Humans Data Accuracy Mandibular Nerve Injuries Radiography, Panoramic Mandibular Nerve Male Tooth Extraction Middle Aged Deep Learning Aged Molar, Third Young Adult Cone-Beam Computed Tomography Adult Mandible Clinical Decision-Making Female
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Assessment of an Artificial Intelligence Mandibular Osteotomy Design System: A Retrospective Study.

Aesthetic plastic surgery Jan 20, 2022
BACKGROUND: In this study, an AI osteotomy software was developed to design the presurgical plan of mandibular angle osteotomy, which is followed by the comparison between the software-designed presurgical plan and the traditional manual presurgical ...
Female Mandible Treatment Outcome Osteotomy Mandibular Osteotomy Humans Artificial Intelligence Retrospective Studies
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Evaluation of multi-task learning in deep learning-based positioning classification of mandibular third molars.

Scientific reports Jan 13, 2022
Pell and Gregory, and Winter's classifications are frequently implemented to classify the mandibular third molars and are crucial for safe tooth extraction. This study aimed to evaluate the classification accuracy of convolutional neural network (CNN...
Mandible Area Under Curve Radiography, Panoramic Neural Networks, Computer Deep Learning Molar, Third Tooth Extraction Tooth, Impacted Humans
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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.
Mandible Cephalometry Machine Learning
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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.
Mandible Image Processing, Computer-Assisted Artificial Intelligence Female Reproducibility of Results Young Adult Adult Male 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 ...
Deep Learning Dental Pulp Cavity Tooth Root Cone-Beam Computed Tomography Mandible Molar
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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 Orthognathic Surgical Procedures Infant Mandible Deep Learning Humans Radiography, Panoramic
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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.
Deep Learning Cone-Beam Computed Tomography Mandible Image Processing, Computer-Assisted Humans Artificial Intelligence
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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...
Dental Caries Dental Caries Susceptibility Data Accuracy Molar, Third Mandible Risk Factors Retrospective Studies Humans Young Adult Tooth, Impacted Clinical Decision-Making Adult Machine Learning Tooth Cervix Female Sensitivity and Specificity Cross-Sectional Studies Male
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