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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Thanathornwong B. | |
dc.contributor.author | Suebnukarn S. | |
dc.contributor.author | Ouivirach K. | |
dc.date.accessioned | 2021-04-05T03:24:04Z | - |
dc.date.available | 2021-04-05T03:24:04Z | - |
dc.date.issued | 2016 | |
dc.identifier.issn | 1692607 | |
dc.identifier.other | 2-s2.0-84958947637 | |
dc.identifier.uri | https://ir.swu.ac.th/jspui/handle/123456789/13463 | - |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84958947637&doi=10.1016%2fj.cmpb.2015.11.004&partnerID=40&md5=5000d9eaa7cc28c7bb811cc7d35e523c | |
dc.description.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. | |
dc.subject | Artificial intelligence | |
dc.subject | Color | |
dc.subject | Decision support systems | |
dc.subject | Forecasting | |
dc.subject | Patient treatment | |
dc.subject | Clinical decision support systems | |
dc.subject | Color changes | |
dc.subject | Color difference | |
dc.subject | Gold standards | |
dc.subject | Multiple regression equations | |
dc.subject | Multiple regressions | |
dc.subject | Post treatment | |
dc.subject | Tooth whitening | |
dc.subject | Colorimetry | |
dc.subject | decision support system | |
dc.subject | gold standard | |
dc.subject | human | |
dc.subject | multiple regression | |
dc.subject | tooth | |
dc.subject | tooth color | |
dc.subject | clinical decision support system | |
dc.subject | color | |
dc.subject | dental procedure | |
dc.subject | Color | |
dc.subject | Decision Support Systems, Clinical | |
dc.subject | Humans | |
dc.subject | Tooth Bleaching | |
dc.title | Decision support system for predicting color change after tooth whitening | |
dc.type | Article | |
dc.rights.holder | Scopus | |
dc.identifier.bibliograpycitation | Computer Methods and Programs in Biomedicine. Vol 125, (2016), p.88-93 | |
dc.identifier.doi | 10.1016/j.cmpb.2015.11.004 | |
Appears in Collections: | Scopus 1983-2021 |
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