Predicting the Debonding of CAD/CAM Composite Resin Crowns with AI

Yamaguchi, S.; Lee, C.; Karaer, O.; Ban, S.; Mine, A.; Imazato, S.

doi:https://doi.org/10.1177/0022034519867641

Predicting the Debonding of CAD/CAM Composite Resin Crowns with AI

Yamaguchi, S.; Lee, C.; Karaer, O. et al.

Journal of Dental Research, 2019, 98(11), 1234-1238

アクセス数:60件（2025-04-22 06:07 集計）

固定URL: https://hdl.handle.net/11094/93083

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タイトル	Predicting the Debonding of CAD/CAM Composite Resin Crowns with AI
著者	NRID 1000030397773 1000030397773 ORCID 0000-0002-4661-8821 0000-0002-4661-8821 researchmap permalink YamaguchiS_30397773 YamaguchiS_30397773 Scopus Author ID 7403873463 7403873463 著者名 Yamaguchi, S.
	著者名 Lee, C.
	著者名 Karaer, O.
	著者名 Ban, S.
	著者名 Mine, A.
	NRID 1000080243244 1000080243244 ORCID 0000-0003-3655-4809 0000-0003-3655-4809 researchmap permalink read0014210 read0014210 Scopus Author ID 55994423600 55994423600 著者名 Imazato, S.
キーワード等	artificial intelligence
	composite materials
	prosthetic dentistry/prosthodontics
	resin(s)
	restorative dentistry
	restorative materials
内容	Yamaguchi S., Lee C., Karaer O., et al. Predicting the Debonding of CAD/CAM Composite Resin Crowns with AI. Journal of Dental Research, 98(11), 1234-1238. © 2019 Sage Publications. DOI: 10.1177/0022034519867641.
抄録	A preventive measure for debonding has not been established and is highly desirable to improve the survival rate of computer-aided design/computer-aided manufacturing (CAD/CAM) composite resin (CR) crowns. The aim of this study was to assess the usefulness of deep learning with a convolution neural network (CNN) method to predict the debonding probability of CAD/CAM CR crowns from 2-dimensional images captured from 3-dimensional (3D) stereolithography models of a die scanned by a 3D oral scanner. All cases of CAD/CAM CR crowns were manufactured from April 2014 to November 2015 at the Division of Prosthodontics, Osaka University Dental Hospital (Ethical Review Board at Osaka University, approval H27-E11). The data set consisted of a total of 24 cases: 12 trouble-free and 12 debonding as known labels. A total of 8,640 images were randomly divided into 6,480 training and validation images and 2,160 test images. Deep learning with a CNN method was conducted to develop a learning model to predict the debonding probability. The prediction accuracy, precision, recall, F-measure, receiver operating characteristic, and area under the curve of the learning model were assessed for the test images. Also, the mean calculation time was measured during the prediction for the test images. The prediction accuracy, precision, recall, and F-measure values of deep learning with a CNN method for the prediction of the debonding probability were 98.5%, 97.0%, 100%, and 0.985, respectively. The mean calculation time was 2 ms/step for 2,160 test images. The area under the curve was 0.998. Artificial intelligence (AI) technology—that is, the deep learning with a CNN method established in this study—demonstrated considerably good performance in terms of predicting the debonding probability of a CAD/CAM CR crown with 3D stereolithography models of a die scanned from patients.
出版者	SAGE Publications Inc.
収録物名	Journal of Dental Research
巻(号)	98(11)
ページ	1234-1238
刊行年月	2019-10-01
言語	英語
ハンドルURL	https://hdl.handle.net/11094/93083
PISSN	00220345
eISSN	15440591
関連情報 (isVersionOf)	DOI (出版社版) https://doi.org/10.1177/0022034519867641
アクセス権	open access
権利情報	© International & American Associations for Dental Research 2019.
出版タイプ	Accepted Manuscript
カテゴリ	学術雑誌論文 Journal Article

資源タイプ	学術雑誌論文
ローカル資源タイプ	学術雑誌論文
DCMI資源タイプ	text
DCTERMS.bibliographicCitation	Journal of Dental Research. 2019, 98(11), p. 1234-1238
DC.title	Predicting the Debonding of CAD/CAM Composite Resin Crowns with AI
DC.creator	Yamaguchi, S.
	Lee, C.
	Karaer, O.
	Ban, S.
	Mine, A.
	Imazato, S.
DC.publisher	SAGE Publications Inc.
DC.language' scheme='DCTERMS.RFC1766	英語
DC.type' scheme='DCTERMS.DCMIType	text
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2019-10-01
DC.identifier	https://doi.org/10.1177/0022034519867641
DC.subject	artificial intelligence
	composite materials
	prosthetic dentistry/prosthodontics
	resin(s)
	restorative dentistry
	restorative materials
DC.description	Yamaguchi S., Lee C., Karaer O., et al. Predicting the Debonding of CAD/CAM Composite Resin Crowns with AI. Journal of Dental Research, 98(11), 1234-1238. © 2019 Sage Publications. DOI: 10.1177/0022034519867641.
DCTERMS.abstract	A preventive measure for debonding has not been established and is highly desirable to improve the survival rate of computer-aided design/computer-aided manufacturing (CAD/CAM) composite resin (CR) crowns. The aim of this study was to assess the usefulness of deep learning with a convolution neural network (CNN) method to predict the debonding probability of CAD/CAM CR crowns from 2-dimensional images captured from 3-dimensional (3D) stereolithography models of a die scanned by a 3D oral scanner. All cases of CAD/CAM CR crowns were manufactured from April 2014 to November 2015 at the Division of Prosthodontics, Osaka University Dental Hospital (Ethical Review Board at Osaka University, approval H27-E11). The data set consisted of a total of 24 cases: 12 trouble-free and 12 debonding as known labels. A total of 8,640 images were randomly divided into 6,480 training and validation images and 2,160 test images. Deep learning with a CNN method was conducted to develop a learning model to predict the debonding probability. The prediction accuracy, precision, recall, F-measure, receiver operating characteristic, and area under the curve of the learning model were assessed for the test images. Also, the mean calculation time was measured during the prediction for the test images. The prediction accuracy, precision, recall, and F-measure values of deep learning with a CNN method for the prediction of the debonding probability were 98.5%, 97.0%, 100%, and 0.985, respectively. The mean calculation time was 2 ms/step for 2,160 test images. The area under the curve was 0.998. Artificial intelligence (AI) technology—that is, the deep learning with a CNN method established in this study—demonstrated considerably good performance in terms of predicting the debonding probability of a CAD/CAM CR crown with 3D stereolithography models of a die scanned from patients.
DC.rights	© International & American Associations for Dental Research 2019.
DC.format	93083
citation_title	Predicting the Debonding of CAD/CAM Composite Resin Crowns with AI
citation_author	Yamaguchi, S.
	Lee, C.
	Karaer, O.
	Ban, S.
	Mine, A.
	Imazato, S.
citation_publisher	SAGE Publications Inc.
citation_language	英語
citation_date	2019-10-01
citation_journal_title	Journal of Dental Research
citation_volume	98
citation_issue	11
citation_firstpage	1234
citation_lastpage	1238
citation_doi	https://doi.org/10.1177/0022034519867641
citation_public_url	https://hdl.handle.net/11094/93083

Predicting the Debonding of CAD/CAM Composite Resin Crowns with AI

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