Please use this identifier to cite or link to this item: https://ir.swu.ac.th/jspui/handle/123456789/13463
Title: Decision support system for predicting color change after tooth whitening
Authors: Thanathornwong B.
Suebnukarn S.
Ouivirach K.
Keywords: Artificial intelligence
Color
Decision support systems
Forecasting
Patient treatment
Clinical decision support systems
Color changes
Color difference
Gold standards
Multiple regression equations
Multiple regressions
Post treatment
Tooth whitening
Colorimetry
decision support system
gold standard
human
multiple regression
tooth
tooth color
clinical decision support system
color
dental procedure
Color
Decision Support Systems, Clinical
Humans
Tooth Bleaching
Issue Date: 2016
Abstract: Tooth whitening is becoming increasingly popular among patients and dentists since it is a relatively noninvasive approach. However, the degree of color change after tooth whitening is known to vary substantially between studies. The present study aims to develop a clinical decision support system for predicting color change after in-office tooth whitening. We used the information from patients' data sets, and applied the multiple regression equation of CIELAB color coordinates including L*, a*, and b* of the original tooth color and the color difference (δE) that expresses the color change after tooth whitening. To evaluate the system performance, the patient's post-treatment color was used as "gold standard" to compare with the post-treatment color predicted by the system. There was a high degree of agreement between the patient's post-treatment color and the post-treatment color predicted by the system (kappa value = 0.894). The results obtained have demonstrated that the decision support system is possible to predict the color change obtained using an in-office whitening system using colorimetric values. © 2015 Elsevier Ireland Ltd.
URI: https://ir.swu.ac.th/jspui/handle/123456789/13463
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84958947637&doi=10.1016%2fj.cmpb.2015.11.004&partnerID=40&md5=5000d9eaa7cc28c7bb811cc7d35e523c
ISSN: 1692607
Appears in Collections:Scopus 1983-2021

Files in This Item:
There are no files associated with this item.


Items in SWU repository are protected by copyright, with all rights reserved, unless otherwise indicated.