AIMC Topic: Molar, Third
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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...
Automatic mandibular third molar and mandibular canal relationship determination based on deep learning models for preoperative risk reduction.
Clinical oral investigations
Mar 25, 2025
OBJECTIVES: This study explores the application of deep learning models for classifying the spatial relationship between mandibular third molars and the mandibular canal using cone-beam computed tomography images. Accurate classification of this rela...
Panoramic radiographic features for machine learning based detection of mandibular third molar root and inferior alveolar canal contact.
Scientific reports
Feb 4, 2025
This study uses machine learning (ML) to elucidate the contact relationship between the mandibular third molar (M3M) and the inferior alveolar canal (IAC), leading to three major contributions; (1) The first publicly accessible PR image dataset with ...
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...
Efficacy of a deep learning system for automatic analysis of the comprehensive spatial relationship between the mandibular third molar and inferior alveolar canal on panoramic radiographs.
Oral surgery, oral medicine, oral pathology and oral radiology
Jan 2, 2025
OBJECTIVE: To develop and evaluate a deep learning (DL) system for predicting the contact and relative position relationships between the mandibular third molar (M3) and inferior alveolar canal (IAC) using panoramic radiographs (PRs) for preoperative...
Limitations of panoramic radiographs in predicting mandibular wisdom tooth extraction and the potential of deep learning models to overcome them.
Scientific reports
Dec 28, 2024
Surgeons routinely interpret preoperative radiographic images for estimating the shape and position of the tooth prior to performing tooth extraction. In this study, we aimed to predict the difficulty of lower wisdom tooth extraction using only panor...
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...
An AI-assisted explainable mTMCNN architecture for detection of mandibular third molar presence from panoramic radiography.
International journal of medical informatics
Nov 23, 2024
OBJECTIVE: This study aimed to design and systematically evaluate an architecture, proposed as the Explainable Mandibular Third Molar Convolutional Neural Network (E-mTMCNN), for detecting the presence of mandibular third molars (m-M3) in panoramic r...
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