Analysis of the feasibility of using deep learning for multiclass classification of dental anomalies on panoramic radiographs-Reference-Cited by-同舟云学术

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Analysis of the feasibility of using deep learning for multiclass classification of dental anomalies on panoramic radiographs

Published:2022-11-25 Issue:6 Volume:41 Page:889-895
ISSN:0287-4547
Container-title:Dental Materials Journal
language:en
Short-container-title:Dent. Mater. J.

Author:

OKAZAKI Shota¹,MINE Yuichi¹,IWAMOTO Yuko²,URABE Shiho¹,MITSUHATA Chieko²,NOMURA Ryota²,KAKIMOTO Naoya³,MURAYAMA Takeshi¹

Affiliation:

1. Department of Medical System Engineering, Graduate School of Biomedical and Health Sciences, Hiroshima University

2. Department of Pediatric Dentistry, Graduate School of Biomedical and Health Sciences, Hiroshima University

3. Department of Oral and Maxillofacial Radiology, Graduate School of Biomedical and Health Sciences, Hiroshima University

Publisher

Japanese Society for Dental Materials and Devices

Subject

General Dentistry,Ceramics and Composites

Link

https://www.jstage.jst.go.jp/article/dmj/41/6/41_2022-098/_pdf

Reference34 articles.

1. 1) Muehlematter UJ, Daniore P, Vokinger KN. Approval of artificial intelligence and machine learning-based medical devices in the USA and Europe (2015-20): A comparative analysis. Lancet Digit Health 2021; 3: e195-e203.

2. 2) Aisu N, Miyake M, Takeshita K, Akiyama M, Kawasaki R, Kashiwagi K, et al. Regulatory-approved deep learning/ machine learning-based medical devices in Japan as of 2020: A systematic review. PLOS Digit Health 2022; 1: e0000001.

3. 3) Yamaguchi S, Lee C, Karaer O, Ban S, Mine A, Imazato S. Predicting the debonding of CAD/CAM composite resin crowns with AI. J Dent Res 2019; 98: 1234-1238.

4. 4) Mine Y, Suzuki S, Eguchi T, Murayama T. Applying deep artificial neural network approach to maxillofacial prostheses coloration. J Prosthodont Res 2020; 64: 296-300.

5. 5) Takahashi T, Nozaki K, Gonda T, Ikebe K. A system for designing removable partial dentures using artificial intelligence. Part 1. Classification of partially edentulous arches using a convolutional neural network. J Prosthodont Res 2021; 65: 115-118.

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