Object's details: Machine learning methods as an aid in planning orthodontic treatment on the example of Cone-Beam Computed Tomography analysis: a literature review

Provider:Akademicka Platforma Czasopism

Description

Title:
Machine learning methods as an aid in planning orthodontic treatment on the example of Cone-Beam Computed Tomography analysis: a literature review
Creator:
Płotka, Szymon;
Włodarczyk, Tomasz;
Szczerba, Ryszard;
Chomiak, Anna;
Komisarek, Oskar
Description:
Convolutional neural networks (CNNs) are used in many areas of computer vision, such as object tracking and recognition, security, military, and biomedical image analysis. In this work, we describe the current methods, the architectures of deep convolutional neural networks used in CBCT. Literature from 2000-2020 from the PubMed database, Google Scholar, was analyzed. Account has been taken of publications in English that describe architectures of deep convolutional neural networks used in CBCT. The results of the reviewed studies indicate that deep learning methods employed in orthodontics can be far superior in comparison to other high-performing algorithms.
Object availability:
free access
Rights:
Prawa autorskie (c) 2021 © The Author(s)
Date:
2021.01.23
Type:
info:eu-repo/semantics/article;
info:eu-repo/semantics/publishedVersion
Language:
English
Source:
Journal of Education, Health and Sport; Vol. 11 No. 1 (2021); 94-104;
Journal of Education, Health and Sport; Tom 11 Nr 1 (2021); 94-104;
2391-8306
Format:
application/pdf
Publisher:
Nicolaus Copernicus University, Toruń, Poland
Relation: https://apcz.umk.pl/JEHS/article/view/JEHS.2021.11.01.010/27911
Subject:
CBCT;
Cone-Beam Computed Tomography;
deep learning;
machine learning;
orthodontics
Identifier: https://apcz.umk.pl/JEHS/article/view/JEHS.2021.11.01.010
Data provider:
Akademicka Platforma Czasopism

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Object's details: Machine learning methods as an aid in planning orthodontic treatment on the example of Cone-Beam Computed Tomography analysis: a literature review

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