AIMC Topic: Tooth, Impacted
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Deep learning-based approach to third molar impaction analysis with clinical classifications.
Scientific reports
Jul 3, 2025
This study developed a deep learning model for the automated detection and classification of impacted third molars using the Pell and Gregory Classification, Winter's Classification, and Pederson Difficulty Index. Panoramic radiographs of patients tr...
Impacted lower third molar classification and difficulty index assessment: comparisons among dental students, general practitioners and deep learning model assistance.
BMC oral health
Jan 28, 2025
BACKGROUND: Assessing the difficulty of impacted lower third molar (ILTM) surgical extraction is crucial for predicting postoperative complications and estimating procedure duration. The aim of this study was to evaluate the effectiveness of a convol...
Automated Cone Beam Computed Tomography Segmentation of Multiple Impacted Teeth With or Without Association to Rare Diseases: Evaluation of Four Deep Learning-Based Methods.
Orthodontics & craniofacial research
Jan 2, 2025
OBJECTIVE: To assess the accuracy of three commercially available and one open-source deep learning (DL) solutions for automatic tooth segmentation in cone beam computed tomography (CBCT) images of patients with multiple dental impactions.
Evaluation of the effectiveness of panoramic radiography in impacted mandibular third molars on deep learning models developed with findings obtained with cone beam computed tomography.
Oral radiology
Dec 27, 2024
OBJECTIVE: The aim of this study is to determine the contact relationship and position of impacted mandibular third molar teeth (IMM) with the mandibular canal (MC) in panoramic radiography (PR) images using deep learning (DL) models trained with the...
Evaluation of the mandibular canal and the third mandibular molar relationship by CBCT with a deep learning approach.
Oral radiology
Dec 11, 2024
OBJECTIVE: The mandibular canal (MC) houses the inferior alveolar nerve. Extraction of the mandibular third molar (MM3) is a common dental surgery, often complicated by nerve damage. CBCT is the most effective imaging method to assess the relationshi...
Development and validation of a deep learning algorithm for the classification of the level of surgical difficulty in impacted mandibular third molar surgery.
International journal of oral and maxillofacial surgery
Dec 4, 2024
The aim of this study was to develop and validate a convolutional neural network (CNN) algorithm for the detection of impacted mandibular third molars in panoramic radiographs and the classification of the surgical extraction difficulty level. A data...
Predicting the Risk of Maxillary Canine Impaction Based on Maxillary Measurements Using Supervised Machine Learning.
Orthodontics & craniofacial research
Oct 4, 2024
OBJECTIVES: To predict palatally impacted maxillary canines based on maxilla measurements through supervised machine learning techniques.
A hierarchical deep learning approach for diagnosing impacted canine-induced root resorption via cone-beam computed tomography.
BMC oral health
Aug 23, 2024
OBJECTIVES: Canine-induced root resorption (CIRR) is caused by impacted canines and CBCT images have shown to be more accurate in diagnosing CIRR than panoramic and periapical radiographs with the reported AUCs being 0.95, 0.49, and 0.57, respectivel...
Can ChatGPT-4o provide new systematic review ideas to oral and maxillofacial surgeons?
Journal of stomatology, oral and maxillofacial surgery
Jul 26, 2024
OBJECTIVE: This study aims to evaluate the capacity of ChatGPT-4o to generate new systematic review ideas in the field of oral and maxillofacial surgery. The data obtained from this study will provide evidence-based information to oral and maxillofac...
Deep Learning for Predicting the Difficulty Level of Removing the Impacted Mandibular Third Molar.
International dental journal
Jul 22, 2024
BACKGROUND: Preoperative assessment of the impacted mandibular third molar (LM3) in a panoramic radiograph is important in surgical planning. The aim of this study was to develop and evaluate a computer-aided visualisation-based deep learning (DL) sy...
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