PłotkaSzymon, Włodarczyk Tomasz, Szczerba Ryszard, Chomiak Anna,Komisarek Oskar. Machine learning methods as an aid in planning orthodontic treatment on the example of Cone-Beam Computed Tomography analysis: a literature review.Journal of Education, Health and Sport.2021;11(01):94-104. eISSN 2391-8306. DOIhttp://dx.doi.org/10.12775/JEHS.2021.11.01.010 https://apcz.umk.pl/czasopisma/index.php/JEHS/article/view/JEHS.2021.11.01.010 https://zenodo.org/record/4459147 The journal has had 5 points in Ministry of Science and Higher Education parametric evaluation. § 8. 2) and § 12. 1. 2) 22.02.2019. © The Authors 2021; This article is published with open access at Licensee Open Journal Systems of Nicolaus Copernicus University in Torun, Poland Open Access. This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author (s) and source are credited. This is an open access article licensed under the terms of the Creative Commons Attribution Non commercial license Share alike. (http://creativecommons.org/licenses/by-nc-sa/4.0/) which permits unrestricted, non commercial use, distribution and reproduction in any medium, provided the work is properly cited. The authors declare that there is no conflict of interests regarding the publication of this paper. Received: 25.12.2020. Revised: 30.12.2020. Accepted: 17.01.2021. Machine learning methods as an aid in planning orthodontic treatment on the example of Cone-Beam Computed Tomography analysis: a literature review Szymon Płotka[1], Tomasz Włodarczyk [1], Ryszard Szczerba [1], Anna Chomiak [2], Oskar Komisarek [2] 1 Institute of Informatics, Warsaw University of Technology, Warsaw, Poland 2 Department of Maxillofacial Orthopaedics and Orthodontics, Poznan University of Medical Sciences, ul. Bukowska 70, 60–812 Poznań, Poland Abstract Convolutional neural networks (CNNs) are used in many areas of computer vision, such as objecttrackingandrecognition,security,military,andbiomedicalimageanalysis.Inthiswork, 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.AccounthasbeentakenofpublicationsinEnglishthatdescribearchitecturesofdeep 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-performingalgorithms. Key words: CBCT; Cone-Beam Computed Tomography; deep learning; machine learning; orthodontics