AIMC Topic: Cephalometry

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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...
Humans Artificial Intelligence Female Mandible Anatomic Landmarks Cephalometry Skull Adult Male Cone-Beam Computed Tomography Imaging, Three-Dimensional
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CephTransX: An attention enhanced feature fusion network leveraging neighborhood rough set approach for cephalometric landmark prediction.

Computers in biology and medicine Feb 25, 2025
The convergence of medical imaging, computer vision, and orthodontics has made automatic cephalometric landmark detection a pivotal area of research. Accurate cephalometric analysis is crucial in orthodontics, orthognathic and maxillofacial surgery f...
Algorithms Image Processing, Computer-Assisted Anatomic Landmarks Humans Cephalometry Neural Networks, Computer
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Accuracy of cephalometric landmark identification by artificial intelligence platform versus expert orthodontist in unilateral cleft palate patients: A retrospective study.

International orthodontics Feb 19, 2025
OBJECTIVE: The primary aim of the study was to evaluate the accuracy of automated artificial intelligence (AI) cephalometric landmark identification in cleft patients and compare it to landmarks identified by an expert orthodontist. The secondary obj...
Humans Artificial Intelligence Adolescent Child Anatomic Landmarks Cephalometry Software Retrospective Studies Female Male Cleft Palate Orthodontists
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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...
Mandibular Condyle Orthodontic Appliances, Functional Cephalometry Humans Mandibular Advancement Artificial Intelligence Malocclusion, Angle Class II Female Male Child Cone-Beam Computed Tomography Imaging, Three-Dimensional Mandible
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Development of a diagnostic classification model for lateral cephalograms based on multitask learning.

BMC oral health Feb 15, 2025
OBJECTIVES: This study aimed to develop a cephalometric classification method based on multitask learning for eight diagnostic classifications.
Machine Learning Retrospective Studies Female Sensitivity and Specificity Male Cephalometry Humans Neural Networks, Computer
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Automated orthodontic diagnosis via self-supervised learning and multi-attribute classification using lateral cephalograms.

Biomedical engineering online Feb 4, 2025
BACKGROUND: Malocclusion, characterized by dental misalignment and improper occlusal relationships, significantly impacts oral health and daily functioning, with a global prevalence of 56%. Lateral cephalogram is a crucial diagnostic tool in orthodon...
Malocclusion Orthodontics Cephalometry Supervised Machine Learning Automation Humans Image Processing, Computer-Assisted
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Integrating machine learning for treatment decisions in anterior open bite orthodontic cases: A retrospective study.

Journal of the World federation of orthodontists Jan 31, 2025
INTRODUCTION: This article explores the integration of machine learning (ML) algorithms to aid in treatment planning and extraction decisions for anterior open bite cases, leveraging demographic, clinical, and radiographic data to predict treatment o...
Open Bite Support Vector Machine Clinical Decision-Making Cephalometry Female Humans Neural Networks, Computer Tooth Extraction Male Young Adult Machine Learning Adolescent Patient Care Planning Adult Algorithms Child Retrospective Studies Treatment Outcome
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3D face reconstruction for maxillofacial surgery based on morphable models and neural networks: A preliminary assessment for anthropometry accuracy.

Journal of cranio-maxillo-facial surgery : official publication of the European Association for Cranio-Maxillo-Facial Surgery Jan 28, 2025
OBJECTIVES: This study aimed to evaluate the anthropometric accuracy of 3D face reconstruction based on neural networks (3DFRBN) using 2D images, including the assessment of global errors and landmarks, as well as linear and angular measurements.
Anatomic Landmarks Cephalometry Healthy Volunteers Anthropometry Young Adult Face Humans Adult Models, Anatomic Male Neural Networks, Computer Surgery, Oral Imaging, Three-Dimensional Female
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Research of orthodontic soft tissue profile prediction based on conditional generative adversarial networks.

Journal of dentistry Jan 24, 2025
OBJECTIVE: This study constructed a new conditional generative adversarial network (CGAN) model to predict changes in lateral appearance following orthodontic treatment.
Adult Male Deep Learning Anatomic Landmarks Humans Neural Networks, Computer Cephalometry Female Face Young Adult Orthodontics, Corrective Generative Adversarial Networks
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Long-Term Predictive Modelling of the Craniofacial Complex Using Machine Learning on 2D Cephalometric Radiographs.

International dental journal Jan 5, 2025
OBJECTIVE: This study aimed to predict long-term growth-related changes in skeletal and dental relationships within the craniofacial complex using machine learning (ML) models.
Adolescent Development Cephalometry Puberty Humans Random Forest Regression Analysis Adolescent Predictive Learning Models Retrospective Studies Predictive Value of Tests Face Child Reproducibility of Results Radiography Random Allocation Mandible Male Incisor Body Size Maxilla Skull Base
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