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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 |
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