AIMC Topic: Cephalometry

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Automatic 3D cephalometric annotation system using shadowed 2D image-based machine learning.

Physics in medicine and biology Feb 20, 2019
This paper presents a new approach to automatic three-dimensional (3D) cephalometric annotation for diagnosis, surgical planning, and treatment evaluation. There has long been considerable demand for automated cephalometric landmarking, since manual ...
Cephalometry Automation Humans Imaging, Three-Dimensional Anatomic Landmarks Machine Learning Reproducibility of Results
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Artificial intelligence assistance for fetal head biometry: Assessment of automated measurement software.

Diagnostic and interventional imaging Sep 1, 2018
PURPOSE: To evaluate the feasibility and reproducibility of artificial intelligence software (Smartplanes) to automatically identify the transthalamic plane from 3D ultrasound volumes and to measure the biparietal diameter (BPD) and head circumferenc...
Feasibility Studies Fetus Reproducibility of Results Ultrasonography, Prenatal Cephalometry Software Pregnancy Female Humans Artificial Intelligence
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Machine learning applied to neuroimaging for diagnosis of adult classic Chiari malformation: role of the basion as a key morphometric indicator.

Journal of neurosurgery Oct 20, 2017
OBJECTIVE The current diagnostic criterion for Chiari malformation Type I (CM-I), based on tonsillar herniation (TH), includes a diversity of patients with amygdalar descent that may be caused by a variety of factors. In contrast, patients presenting...
Humans Machine Learning Arnold-Chiari Malformation Skull Base Amygdala Encephalocele Foramen Magnum Diagnosis, Differential Adult Cephalometry Logistic Models Neuroimaging Decision Trees
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Use of automated learning techniques for predicting mandibular morphology in skeletal class I, II and III.

Forensic science international Oct 12, 2017
BACKGROUND: The prediction of the mandibular bone morphology in facial reconstruction for forensic purposes is usually performed considering a straight profile corresponding to skeletal class I, with application of linear and parametric analysis whic...
Support Vector Machine Regression Analysis Anatomic Landmarks Cephalometry Humans Malocclusion Male Neural Networks, Computer Adolescent Female Mandible Young Adult Adult
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An automatic method for skeletal patterns classification using craniomaxillary variables on a Colombian population.

Forensic science international Dec 25, 2015
BACKGROUND: The mandibular bone is an important part of the forensic facial reconstruction and it has the possibility of getting lost in skeletonized remains; for this reason, it is necessary to facilitate the identification process simulating the ma...
Adolescent Image Processing, Computer-Assisted Support Vector Machine Prospective Studies Young Adult Female Skull Anatomic Landmarks Humans Adult Forensic Anthropology Cephalometry Male Colombia
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A knowledge-based algorithm for automatic detection of cephalometric landmarks on CBCT images.

International journal of computer assisted radiology and surgery Apr 7, 2015
PURPOSE: Cone-beam computed tomography (CBCT) is now an established component for 3D evaluation and treatment planning of patients with severe malocclusion and craniofacial deformities. Precision landmark plotting on 3D images for cephalometric analy...
Cephalometry Cone-Beam Computed Tomography Skull Humans Knowledge Bases Imaging, Three-Dimensional Algorithms Electronic Data Processing Anatomic Landmarks Reproducibility of Results
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THERE: Toward an easy and reliable tool for automatic cephalometric analysis.

Computers in biology and medicine Jun 1, 2025
BACKGROUND: Cephalometric analysis in orthodontics is a meticulous process requiring high precision in identifying anatomical landmarks on lateral cephalometric radiographs. Manual analysis by clinicians remains the standard, as even slight deviation...
Image Processing, Computer-Assisted Humans Anatomic Landmarks Cephalometry Software Orthodontics
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Healthcare resource utilization for the management of neonatal head shape deformities: a propensity-matched analysis of AI-assisted and conventional approaches.

Journal of neurosurgery. Pediatrics Jun 1, 2025
OBJECTIVE: Overuse of radiography studies and underuse of conservative therapies for cranial deformities in neonates is a known inefficiency in pediatric craniofacial healthcare. This study sought to establish whether the introduction of artificial i...
Health Resources Cephalometry Propensity Score Female Infant, Newborn Male Artificial Intelligence Patient Acceptance of Health Care Retrospective Studies Humans
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Real-time segmentation and detection of ponticulus posticus in lateral cephalometric radiographs using YOLOv8: a step towards enhanced clinical evaluation.

BMC oral health May 28, 2025
OBJECTIVES: Ponticulus posticus (PP) is a bony structure in the cervical spine, often difficult to identify in radiographic images, and its detection is important for both orthodontic diagnosis and clinical decision-making related to craniovertebral ...
Retrospective Studies Deep Learning Humans Cervical Vertebrae Cephalometry
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Hierarchical clustering analysis & machine learning models for diagnosing skeletal classes I and II in German patients.

BMC oral health May 15, 2025
BACKGROUND: Classification is one of the most common tasks in artificial intelligence (AI) driven fields in dentistry and orthodontics. The AI abilities can significantly improve the orthodontist's critical mission to diagnose and treat patients prec...
Machine Learning Female Reproducibility of Results Humans Male Adolescent Support Vector Machine Cluster Analysis Young Adult Adult Sensitivity and Specificity Malocclusion, Angle Class II Malocclusion, Angle Class I Discriminant Analysis Cephalometry Germany
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