AIMC Topic: Mandible
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Development and verification of a convolutional neural network-based model for automatic mandibular canal localization on multicenter CBCT images.
BMC oral health
Aug 21, 2025
OBJECTIVES: Development and verification of a convolutional neural network (CNN)-based deep learning (DL) model for mandibular canal (MC) localization on multicenter cone beam computed tomography (CBCT) images.
Applying deep learning techniques to identify tonsilloliths in panoramic radiography.
Scientific reports
Jul 9, 2025
Tonsilloliths can be seen on panoramic radiographs (PRs) as deposits located on the middle portion of the ramus of the mandible. Although tonsilloliths are clinically harmless, the high risk of misdiagnosis leads to unnecessary advanced examinations ...
Comparison of 2D, 2.5D, and 3D segmentation networks for mandibular canals in CBCT images: a study on public and external datasets.
BMC oral health
Jul 8, 2025
The purpose of this study was to compare the performances of 2D, 2.5D, and 3D CNN-based segmentation networks, along with a 3D vision transformer-based segmentation network, for segmenting mandibular canals (MCs) on the public and external CBCT datas...
AI-powered segmentation of bifid mandibular canals using CBCT.
BMC oral health
Jun 4, 2025
OBJECTIVE: Accurate segmentation of the mandibular and bifid canals is crucial in dental implant planning to ensure safe implant placement, third molar extractions and other surgical interventions. The objective of this study is to develop and valida...
Automated landmark-based mid-sagittal plane: reliability for 3-dimensional mandibular asymmetry assessment on head CT scans.
Clinical oral investigations
May 26, 2025
OBJECTIVE: The determination of the mid-sagittal plane (MSP) on three-dimensional (3D) head imaging is key to the assessment of facial asymmetry. The aim of this study was to evaluate the reliability of an automated landmark-based MSP to quantify man...
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...
Accuracy of artificial intelligence-based segmentation in maxillofacial structures: a systematic review.
BMC oral health
Mar 7, 2025
OBJECTIVE: The aim of this review was to evaluate the accuracy of artificial intelligence (AI) in the segmentation of teeth, jawbone (maxilla, mandible with temporomandibular joint), and mandibular (inferior alveolar) canal in CBCT and CT scans.
Artificial intelligence-assisted identification and assessment of mandibular asymmetry on panoramic radiography.
American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics
Mar 6, 2025
INTRODUCTION: Mandibular symmetry is crucial in orthodontic diagnosis and treatment planning. This study aimed to establish an artificial intelligence (AI) method to automatically and accurately identify mandibular landmarks and assess asymmetry via ...
Development of Artificial Intelligence-Supported Automatic Three-Dimensional Surface Cephalometry.
Orthodontics & craniofacial research
Mar 4, 2025
OBJECTIVE: Surface-based three-dimensional (3D) cephalometry provides detailed clinical information for the analysis of craniofacial structures. This study aimed to develop an automated 3D surface cephalometry system using mesh fitting based on landm...
Three-dimensional analysis of mandibular and condylar growth using artificial intelligence tools: a comparison of twin-block and Frankel II Appliances.
BMC oral health
Feb 18, 2025
BACKGROUND: Analyzing the morphological growth changes upon mandibular advancement between Twin Block (TB) and Functional Regulator II (FR2) in Class II patients involves measuring the condylar and mandibular changes in terms of linear and volumetric...
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