Journal of Dental Implant Research 2020; 39(4): 48-52  
Application of artificial intelligence in identification of dental implants system: literature review
Ho-Kyung Lim , Yeh-Jin Kwon, Eui-Seok Lee
Department of Oral and Maxillofacial Surgery, Korea University Guro Hospital, Seoul, Korea
Correspondence to: Ho-Kyung Lim, https://orcid.org/0000-0003-4083-7721
Department of Oral and Maxillofacial Surgery, Korea University Guro Hospital, 148 Gurodong-ro, Guro-gu, Seoul 08308, Korea. Tel: +82-2-2626-1520, Fax: +82-2-837-6245, E-mail: ungassi@naver.com
Received: November 11, 2020; Revised: November 16, 2020; Accepted: November 17, 2020; Published online: December 30, 2020.
© The Korean Academy of Implant Dentistry. All rights reserved.

This is an open access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Artificial intelligence (AI) can be used for broad field and have also applied in dental field. Although there have not been many studies on AI related to dental implants, research to identify implant systems in radiographic images using AI is being tried actively. The purpose of this study is to review articles related to application of AI for identification of dental implants system. A systematic review was conducted using Pubmed and Scopus databases to identify articles about AI and dental implants system in English literature. Factors such as Implant system, Data modality, Sample Size, Training time, Validation method, AI method, Accuracy, Sensitivity, Specificity, and Area under the receiver operating characteristic curve (AUC) were extracted from 4 articles. In the literatures, all of the research adopted to pretrained convolutional neural networks as AI method. AI accuracy to find correct implant system was ranged from 51 to 99.5 percent.
Keywords: Artificial intelligence, Machine learning, Deep learning, Dental implant


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