AIMC Topic: Mandible

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Deep-learning-based automatic facial bone segmentation using a two-dimensional U-Net.

International journal of oral and maxillofacial surgery
The use of deep learning (DL) in medical imaging is becoming increasingly widespread. Although DL has been used previously for the segmentation of facial bones in computed tomography (CT) images, there are few reports of segmentation involving multip...

Deep learning model for analyzing the relationship between mandibular third molar and inferior alveolar nerve in panoramic radiography.

Scientific reports
In this study, the accuracy of the positional relationship of the contact between the inferior alveolar canal and mandibular third molar was evaluated using deep learning. In contact analysis, we investigated the diagnostic performance of the presenc...

Feasibility of a Robot-Assisted Surgical Navigation System for Mandibular Distraction Osteogenesis in Hemifacial Microsomia: A Model Experiment.

The Journal of craniofacial surgery
This study aimed to investigate the feasibility and accuracy of osteotomy and distractor placement using a robotic navigation system in a model surgical experiment of mandibular distraction osteogenesis for hemifacial microsomia. Imaging data from 5 ...

Comparison between cephalometric measurements using digital manual and web-based artificial intelligence cephalometric tracing software.

Dental press journal of orthodontics
OBJECTIVE: The aim of this study was to compare the measurements performed with digital manual (DM) cephalometric analysis and automatic cephalometric analysis obtained from an online artificial intelligence (AI) platform, according to different sagi...

Automatic Detection of Periapical Osteolytic Lesions on Cone-beam Computed Tomography Using Deep Convolutional Neuronal Networks.

Journal of endodontics
INTRODUCTION: Cone-beam computed tomography (CBCT) is an essential diagnostic tool in oral radiology. Radiolucent periapical lesions (PALs) represent the most frequent jaw lesions. However, the description, interpretation, and documentation of radiol...

Construction of a new automatic grading system for jaw bone mineral density level based on deep learning using cone beam computed tomography.

Scientific reports
To develop and verify an automatic classification method using artificial intelligence deep learning to determine the bone mineral density level of the implant site in oral implant surgery from radiographic data obtained from cone beam computed tomog...

An Artificial Intelligence-Based Cosmesis Evaluation for Temporomandibular Joint Reconstruction.

The Laryngoscope
OBJECTIVE: Management of the temporomandibular joint (TMJ) following condylar resection remains challenging in the field of mandibular reconstruction. A simple reconstruction of the TMJ with a contoured end of a fibular graft placed into the joint sp...

Mandibular premolar identification system based on a deep learning model.

Journal of oral biosciences
OBJECTIVES: For constructing an isolated tooth identification system using deep learning, Igarashi et al. (2021) began constructing a learning model as basic research to identify the left and right mandibular first and second premolars. These teeth w...

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
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.

Artificial intelligence in positioning between mandibular third molar and inferior alveolar nerve on panoramic radiography.

Scientific reports
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...