AIMC Topic: Tooth, Impacted
Clear Filters Showing 11 to 20 of 25 articles
Image segmentation of impacted mesiodens using deep learning.
The Journal of clinical pediatric dentistry
May 3, 2024
This study aimed to evaluate the performance of deep learning algorithms for the classification and segmentation of impacted mesiodens in pediatric panoramic radiographs. A total of 850 panoramic radiographs of pediatric patients (aged 3-9 years) was...
Prediction of extraction difficulty for impacted maxillary third molars with deep learning approach.
Journal of stomatology, oral and maxillofacial surgery
Mar 7, 2024
OBJECTIVE: The aim of this study is to determine if a deep learning (DL) model can predict the surgical difficulty for impacted maxillary third molar tooth using panoramic images before surgery.
Deep learning driven segmentation of maxillary impacted canine on cone beam computed tomography images.
Scientific reports
Jan 3, 2024
The process of creating virtual models of dentomaxillofacial structures through three-dimensional segmentation is a crucial component of most digital dental workflows. This process is typically performed using manual or semi-automated approaches, whi...
Developing deep learning methods for classification of teeth in dental panoramic radiography.
Oral surgery, oral medicine, oral pathology and oral radiology
Mar 30, 2023
OBJECTIVES: We aimed to develop an artificial intelligence-based clinical dental decision-support system using deep-learning methods to reduce diagnostic interpretation error and time and increase the effectiveness of dental treatment and classificat...
Deep learning for preliminary profiling of panoramic images.
Oral radiology
Jun 27, 2022
OBJECTIVE: This study explored the feasibility of using deep learning for profiling of panoramic radiographs.
Automatic visualization of the mandibular canal in relation to an impacted mandibular third molar on panoramic radiographs using deep learning segmentation and transfer learning techniques.
Oral surgery, oral medicine, oral pathology and oral radiology
Jun 5, 2022
OBJECTIVE: The aim of this study was to create and assess a deep learning model using segmentation and transfer learning methods to visualize the proximity of the mandibular canal to an impacted third molar on panoramic radiographs.
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...
Diagnostic charting of panoramic radiography using deep-learning artificial intelligence system.
Oral radiology
Oct 5, 2021
OBJECTIVES: The goal of this study was to develop and evaluate the performance of a new deep-learning (DL) artificial intelligence (AI) model for diagnostic charting in panoramic radiography.
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...
Deep learning systems for detecting and classifying the presence of impacted supernumerary teeth in the maxillary incisor region on panoramic radiographs.
Oral surgery, oral medicine, oral pathology and oral radiology
Jun 2, 2020
OBJECTIVE: This investigation aimed to verify and compare the performance of 3 deep learning systems for classifying maxillary impacted supernumerary teeth (ISTs) in patients with fully erupted incisors.
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