AIMC Topic: Cephalometry

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Deep Anatomical Context Feature Learning for Cephalometric Landmark Detection.

IEEE journal of biomedical and health informatics Mar 5, 2021
In the past decade, anatomical context features have been widely used for cephalometric landmark detection and significant progress is still being made. However, most existing methods rely on handcrafted graphical models rather than incorporating ana...
Humans Neural Networks, Computer Radiography Cephalometry
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Machine learning for image-based detection of patients with obstructive sleep apnea: an exploratory study.

Sleep & breathing = Schlaf & Atmung Feb 8, 2021
PURPOSE: In 2-dimensional lateral cephalometric radiographs, patients with severe obstructive sleep apnea (OSA) exhibit a more crowded oropharynx in comparison with non-OSA. We tested the hypothesis that machine learning, an application of artificial...
Adult Sensitivity and Specificity Male Middle Aged Humans Deep Learning Female Radiography Cephalometry Sleep Apnea, Obstructive
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Automatic Cephalometric Landmark Identification System Based on the Multi-Stage Convolutional Neural Networks with CBCT Combination Images.

Sensors (Basel, Switzerland) Jan 12, 2021
This study was designed to develop and verify a fully automated cephalometry landmark identification system, based on multi-stage convolutional neural networks (CNNs) architecture, using a combination dataset. In this research, we trained and tested ...
Anatomic Landmarks Cephalometry Craniofacial Abnormalities Reproducibility of Results Cone-Beam Computed Tomography Imaging, Three-Dimensional Humans Neural Networks, Computer Databases, Factual Retrospective Studies
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Automated Lateral Ventricular and Cranial Vault Volume Measurements in 13,851 Patients Using Deep Learning Algorithms.

World neurosurgery Jan 6, 2021
BACKGROUND: No large dataset-derived standard has been established for normal or pathologic human cerebral ventricular and cranial vault volumes. Automated volumetric measurements could be used to assist in diagnosis and follow-up of hydrocephalus or...
Humans Female Aged Aged, 80 and over Young Adult Algorithms Brain Retrospective Studies Tomography, X-Ray Computed Atrophy Lateral Ventricles Child Natural Language Processing Cephalometry Hydrocephalus Models, Biological Datasets as Topic Neuroimaging Skull Adult Cross-Sectional Studies Male Middle Aged Deep Learning Adolescent Child, Preschool
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Cascaded convolutional networks for automatic cephalometric landmark detection.

Medical image analysis Nov 18, 2020
Cephalometric analysis is a fundamental examination which is widely used in orthodontic diagnosis and treatment planning. Its key step is to detect the anatomical landmarks in lateral cephalograms, which is time-consuming in traditional manual way. T...
Face Neural Networks, Computer Reproducibility of Results Radiography Cephalometry
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Machine learning approaches for sex estimation using cranial measurements.

International journal of legal medicine Nov 11, 2020
The aim of the present study is to apply support vector machines (SVM) and artificial neural network (ANN) as sex classifiers and to generate useful classification models for sex estimation based on cranial measurements. Besides, the performance of t...
Datasets as Topic Sex Determination by Skeleton Skull Bulgaria Logistic Models Support Vector Machine Anatomic Landmarks Cephalometry Humans Male Neural Networks, Computer Middle Aged Female Adolescent Reproducibility of Results Aged Aged, 80 and over Adult
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Automated cephalometric landmark detection with confidence regions using Bayesian convolutional neural networks.

BMC oral health Oct 7, 2020
BACKGROUND: Despite the integral role of cephalometric analysis in orthodontics, there have been limitations regarding the reliability, accuracy, etc. of cephalometric landmarks tracing. Attempts on developing automatic plotting systems have continuo...
Neural Networks, Computer Bayes Theorem Reproducibility of Results Anatomic Landmarks Image Processing, Computer-Assisted Cephalometry
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Data mining for sex estimation based on cranial measurements.

Forensic science international Aug 2, 2020
The aim of the present study is to develop effective and understandable classification models for sex estimation and to identify the most dimorphic linear measurements in adult crania by means of data mining techniques. Furthermore, machine learning ...
Cephalometry Data Mining Sex Determination by Skeleton Skull Forensic Anthropology Tomography, X-Ray Computed Aged Imaging, Three-Dimensional Aged, 80 and over Logistic Models Young Adult Adult Decision Trees Humans Anatomic Landmarks Machine Learning Middle Aged Algorithms Adolescent
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Constructing an automatic diagnosis and severity-classification model for acromegaly using facial photographs by deep learning.

Journal of hematology & oncology Jul 3, 2020
Due to acromegaly's insidious onset and slow progression, its diagnosis is usually delayed, thus causing severe complications and treatment difficulty. A convenient screening method is imperative. Based on our previous work, we herein developed a new...
Datasets as Topic Face Acromegaly Photography Female Middle Aged Image Processing, Computer-Assisted Adult Severity of Illness Index Models, Theoretical Sensitivity and Specificity Humans Diagnosis, Computer-Assisted Cephalometry Male Algorithms Deep Learning Diagnostic Self Evaluation
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Web-based fully automated cephalometric analysis by deep learning.

Computer methods and programs in biomedicine May 6, 2020
BACKGROUND AND OBJECTIVE: An accurate lateral cephalometric analysis is vital in orthodontic diagnosis. Identification of anatomic landmarks on lateral cephalograms is tedious, and errors may occur depending on the doctor's experience. Several attemp...
Cephalometry Deep Learning Radiography Internet Anatomic Landmarks
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