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

Number of Access:672（2025-08-13 23:50 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/93083

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Title
Predicting the Debonding of CAD/CAM Composite Resin Crowns with AI
Creator
NRID
1000030397773 1000030397773
ORCID
0000-0002-4661-8821 0000-0002-4661-8821
researchmap permalink
YamaguchiS_30397773 YamaguchiS_30397773
Scopus Author ID
7403873463 7403873463
CreatorName
Yamaguchi, S.
CreatorName
Lee, C.
CreatorName
Karaer, O.
CreatorName
Ban, S.
CreatorName
Mine, A.
NRID
1000080243244 1000080243244
ORCID
0000-0003-3655-4809 0000-0003-3655-4809
researchmap permalink
read0014210 read0014210
Scopus Author ID
55994423600 55994423600
CreatorName
Imazato, S.
Subject
artificial intelligence
composite materials
prosthetic dentistry/prosthodontics
resin(s)
restorative dentistry
restorative materials
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.
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.
Publisher
SAGE Publications Inc.
Source Title
Journal of Dental Research
Volume (Issue)
98(11)
Page
1234-1238
Date of Issued
2019-10-01
Language
English
Handle URL
https://hdl.handle.net/11094/93083
PISSN
00220345
eISSN
15440591
Relation.isVersionOf
DOI (Publisher Version)
https://doi.org/10.1177/0022034519867641
Access Rights
open access
CopyRight
© International & American Associations for Dental Research 2019.
oaire:version
Accepted Manuscript
Category
学術雑誌論文 Journal Article