Cited 3 times in
A novel model to predict tooth bleaching efficacy using autofluorescence of the tooth
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 김백일 | - |
dc.contributor.author | 이주영 | - |
dc.contributor.author | 정회인 | - |
dc.date.accessioned | 2022-02-23T01:34:49Z | - |
dc.date.available | 2022-02-23T01:34:49Z | - |
dc.date.issued | 2022-01 | - |
dc.identifier.issn | 0300-5712 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/187780 | - |
dc.description.abstract | Objectives: We aimed to confirm whether autofluorescence emitted from teeth can predict tooth bleaching efficacy and establish a novel model combining natural color parameters and tooth autofluorescence data to improve the predictability of tooth bleaching. Methods: A total of 61 tooth specimens were prepared from extracted human molars/premolars and immersed in 35% hydrogen peroxide for 1 h for tooth bleaching. The changes in laser-induced fluorescence (∆LIF) were assessed using Raman spectrometry. Tooth color and autofluorescence data were obtained using quantitative light-induced fluorescence (QLF) technology. Pearson correlation analyses were used to confirm the relationship between ∆LIF and autofluorescence. Intraclass correlation coefficients (ICC) were calculated to compare the conventional and new prediction models. Decision tree analysis was performed to evaluate clinical applicability. Results: The yellowness-to-blueness value from fluorescence imaging showed a moderate correlation with ∆LIF (r= -0.409, p = 0.001). The degree of agreement between the actual efficacy and that predicted by our novel model was high (ICC=0.933, p = 0.002). Decision tree analysis suggested that tooth autofluorescence could be a key factor in prediction of tooth bleaching outcomes. Conclusions: Our findings showed that autofluorescence detected from QLF images may be used to predict tooth bleaching efficacy. Our proposed model appeared to improve the predictability of tooth bleaching. | - |
dc.description.statementOfResponsibility | restriction | - |
dc.language | English | - |
dc.publisher | Elsevier | - |
dc.relation.isPartOf | JOURNAL OF DENTISTRY | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.title | A novel model to predict tooth bleaching efficacy using autofluorescence of the tooth | - |
dc.type | Article | - |
dc.contributor.college | College of Dentistry (치과대학) | - |
dc.contributor.department | Dept. of Preventive Dentistry and Public Oral Health (예방치과학교실) | - |
dc.contributor.googleauthor | Joo-Young Lee | - |
dc.contributor.googleauthor | Hoi-In Jung | - |
dc.contributor.googleauthor | Baek-Il Kim | - |
dc.identifier.doi | 10.1016/j.jdent.2021.103892 | - |
dc.contributor.localId | A00485 | - |
dc.contributor.localId | A05879 | - |
dc.contributor.localId | A03788 | - |
dc.relation.journalcode | J01368 | - |
dc.identifier.eissn | 1879-176X | - |
dc.identifier.pmid | 34798150 | - |
dc.identifier.url | https://www.sciencedirect.com/science/article/pii/S0300571221003146?via%3Dihub | - |
dc.subject.keyword | Autofluorescence | - |
dc.subject.keyword | Prediction | - |
dc.subject.keyword | Quantitative light-induced fluorescence (QLF) technology | - |
dc.subject.keyword | Raman spectrometry | - |
dc.subject.keyword | Tooth bleaching | - |
dc.contributor.alternativeName | Kim, Baek Il | - |
dc.contributor.affiliatedAuthor | 김백일 | - |
dc.contributor.affiliatedAuthor | 이주영 | - |
dc.contributor.affiliatedAuthor | 정회인 | - |
dc.citation.volume | 116 | - |
dc.citation.startPage | 103892 | - |
dc.identifier.bibliographicCitation | JOURNAL OF DENTISTRY, Vol.116 : 103892, 2022-01 | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.