A novel model to predict tooth bleaching efficacy using autofluorescence of the tooth.

Joo-Young Lee, Hoi-In Jung, Baek-Il Kim

Journal of dentistry 2022 Jan

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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. 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. 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. 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. Copyright © 2021 Elsevier Ltd. All rights reserved.

Citation

Joo-Young Lee, Hoi-In Jung, Baek-Il Kim. A novel model to predict tooth bleaching efficacy using autofluorescence of the tooth. Journal of dentistry. 2022 Jan;116:103892