AIMC Topic: Cephalometry
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Development of novel artificial intelligence systems to predict facial morphology after orthognathic surgery and orthodontic treatment in Japanese patients.
Scientific reports
Aug 4, 2021
From a socio-psychological standpoint, improving the morphology of the facial soft-tissues is regarded as an important therapeutic goal in modern orthodontic treatment. Currently, many of the algorithms used in commercially available software program...
Clinical applicability of automated cephalometric landmark identification: Part II - Number of images needed to re-learn various quality of images.
Orthodontics & craniofacial research
Jul 11, 2021
AIM: To estimate the number of cephalograms needed to re-learn for different quality images, when artificial intelligence (AI) systems are introduced in a clinic.
Accuracy of automated identification of lateral cephalometric landmarks using cascade convolutional neural networks on lateral cephalograms from nationwide multi-centres.
Orthodontics & craniofacial research
Jun 27, 2021
OBJECTIVE: To investigate the accuracy of automated identification of cephalometric landmarks using the cascade convolutional neural networks (CNN) on lateral cephalograms acquired from nationwide multi-centres.
Machine learning from clinical data sets of a contemporary decision for orthodontic tooth extraction.
Orthodontics & craniofacial research
Jun 7, 2021
OBJECTIVE: To examine the robustness of the published machine learning models in the prediction of extraction vs non-extraction for a diverse US sample population seen by multiple providers.
Machine learning and orthodontics, current trends and the future opportunities: A scoping review.
American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics
Jun 5, 2021
INTRODUCTION: In recent years, artificial intelligence (AI) has been applied in various ways in medicine and dentistry. Advancements in AI technology show promising results in the practice of orthodontics. This scoping review aimed to investigate the...
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...
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...
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...
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 ...
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...
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