AIMC Topic: Cephalometry

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Comparison of cephalometric measurements between conventional and automatic cephalometric analysis using convolutional neural network.

Progress in orthodontics May 31, 2021
OBJECTIVE: The rapid development of artificial intelligence technologies for medical imaging has recently enabled automatic identification of anatomical landmarks on radiographs. The purpose of this study was to compare the results of an automatic ce...
Neural Networks, Computer Artificial Intelligence Reproducibility of Results Humans Mandible Cephalometry
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Deep learning for cephalometric landmark detection: systematic review and meta-analysis.

Clinical oral investigations May 27, 2021
OBJECTIVES: Deep learning (DL) has been increasingly employed for automated landmark detection, e.g., for cephalometric purposes. We performed a systematic review and meta-analysis to assess the accuracy and underlying evidence for DL for cephalometr...
Deep Learning Radiography Cephalometry Reproducibility of Results
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Three-dimensional virtual planning in mandibular advancement surgery: Soft tissue prediction based on deep learning.

Journal of cranio-maxillo-facial surgery : official publication of the European Association for Cranio-Maxillo-Facial Surgery Apr 21, 2021
The study aimed at developing a deep-learning (DL)-based algorithm to predict the virtual soft tissue profile after mandibular advancement surgery, and to compare its accuracy with the mass tensor model (MTM). Subjects who underwent mandibular advanc...
Chin Deep Learning Imaging, Three-Dimensional Mandibular Advancement Mandible Cephalometry Orthognathic Surgical Procedures Lip Humans
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Image processing and machine learning for telehealth craniosynostosis screening in newborns.

Journal of neurosurgery. Pediatrics Mar 19, 2021
OBJECTIVE: The authors sought to evaluate the accuracy of a novel telehealth-compatible diagnostic software system for identifying craniosynostosis within a newborn (< 1 year old) population. Agreement with gold standard craniometric diagnostics was ...
Telemedicine Image Interpretation, Computer-Assisted Mass Screening Craniosynostoses Cephalometry Humans Machine Learning Software Female Infant, Newborn Male
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Deep learning based prediction of necessity for orthognathic surgery of skeletal malocclusion using cephalogram in Korean individuals.

BMC oral health Mar 18, 2021
BACKGROUND: Posteroanterior and lateral cephalogram have been widely used for evaluating the necessity of orthognathic surgery. The purpose of this study was to develop a deep learning network to automatically predict the need for orthodontic surgery...
Humans Malocclusion Orthognathic Surgical Procedures Malocclusion, Angle Class III Republic of Korea Deep Learning Cephalometry Orthognathic Surgery
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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 Radiography Neural Networks, Computer 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...
Radiography Cephalometry Sleep Apnea, Obstructive Middle Aged Deep Learning Humans Female Adult Sensitivity and Specificity Male
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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 ...
Craniofacial Abnormalities Humans Imaging, Three-Dimensional Neural Networks, Computer Retrospective Studies Anatomic Landmarks Databases, Factual Reproducibility of Results Cephalometry Cone-Beam Computed Tomography
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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...
Deep Learning Adolescent Child, Preschool Child Natural Language Processing Humans Cephalometry Algorithms Cross-Sectional Studies Female Male Lateral Ventricles Aged Young Adult Middle Aged Aged, 80 and over Neuroimaging Brain Adult Hydrocephalus Skull Retrospective Studies Models, Biological Atrophy Tomography, X-Ray Computed Datasets as Topic
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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...
Radiography Neural Networks, Computer Cephalometry Reproducibility of Results Face
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