AIMC Topic: Molar, Third

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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
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
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...

Automatic detection and proximity quantification of inferior alveolar nerve and mandibular third molar on cone-beam computed tomography.

Clinical oral investigations
OBJECTIVES: During mandibular third molar (MTM) extraction surgery, preoperative analysis to quantify the proximity of the MTM to the surrounding inferior alveolar nerve (IAN) is essential to minimize the risk of IAN injury. This study aims to propos...

A two-stage deep-learning model for determination of the contact of mandibular third molars with the mandibular canal on panoramic radiographs.

BMC oral health
OBJECTIVES: This study aimed to assess the accuracy of a two-stage deep learning (DL) model for (1) detecting mandibular third molars (MTMs) and the mandibular canal (MC), and (2) classifying the anatomical relationship between these structures (cont...

Comparison of Faster R-CNN, YOLO, and SSD for Third Molar Angle Detection in Dental Panoramic X-rays.

Sensors (Basel, Switzerland)
The use of artificial intelligence algorithms (AI) has gained importance for dental applications in recent years. Analyzing AI information from different sensor data such as images or panoramic radiographs (panoramic X-rays) can help to improve medic...

Automated classification of mandibular canal in relation to third molar using CBCT images.

F1000Research
BACKGROUND: Dental radiology has significantly benefited from cone-beam computed tomography (CBCT) because of its compact size and low radiation exposure. Canal tracking is an important application of CBCT for determining the relationship between the...

Can ChatGPT-4o provide new systematic review ideas to oral and maxillofacial surgeons?

Journal of stomatology, oral and maxillofacial surgery
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
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...

Automatic detection of the third molar and mandibular canal on panoramic radiographs based on deep learning.

Journal of stomatology, oral and maxillofacial surgery
PURPOSE: This study aims to develop a deep learning framework for the automatic detection of the position relationship between the mandibular third molar (M3) and the mandibular canal (MC) on panoramic radiographs (PRs), to assist doctors in assessin...

A fully automated classification of third molar development stages using deep learning.

Scientific reports
Accurate classification of tooth development stages from orthopantomograms (OPG) is crucial for dental diagnosis, treatment planning, age assessment, and forensic applications. This study aims to develop an automated method for classifying third mola...