STATEMENT OF PROBLEM: There is no scientific information regarding the selection of the color of teeth for edentulous patients. PURPOSE: The purpose of this study was to evaluate linear regression models that may be used to predict color parameters for central incisors of edentulous patients based on some characteristics of dentate subjects. MATERIAL AND METHODS: A spectroradiometer and an external light source were set in a noncontacting 45/0 degree (45-degree illumination and 0-degree observer) optical configuration to measure the color of subjects' vital craniofacial structures (maxillary central incisor, attached gingiva, and facial skin). The subjects (n=120) were stratified into 5 age groups with 4 racial groups and balanced for gender. Linear first-order regression was used to determine the significant factors (alpha=.05) in the prediction model for each color direction of the color of the maxillary central incisor. Age, gender, and color of the other craniofacial structures were studied as potential predictors. Final predictions in each color direction were based only on the statistically significant factors, and then the color differences between observed and predicted CIELAB values for the central incisors were calculated and summarized. RESULTS: The statistically significant predictors of age and gender accounted for 36% of the total variability in L*. The statistically significant predictor of age accounted for 16% of the total variability in a*. The statistically significant predictors of age and gender accounted for 21% of the variability in b*. The mean Delta (SD) between predicted and observed CIELAB values for the central incisor was 5.8 (3.2). CONCLUSIONS: Age and gender were found to be statistically significant determinants in predicting the natural color of central incisors. Although the precision of these predictions was less than the median color difference found for all pairs of teeth studied, and may be considered an acceptable precision, further study is needed to reduce this precision to the limit of detection.
STATEMENT OF PROBLEM: There is no scientific information regarding the selection of the color of teeth for edentulouspatients. PURPOSE: The purpose of this study was to evaluate linear regression models that may be used to predict color parameters for central incisors of edentulouspatients based on some characteristics of dentate subjects. MATERIAL AND METHODS: A spectroradiometer and an external light source were set in a noncontacting 45/0 degree (45-degree illumination and 0-degree observer) optical configuration to measure the color of subjects' vital craniofacial structures (maxillary central incisor, attached gingiva, and facial skin). The subjects (n=120) were stratified into 5 age groups with 4 racial groups and balanced for gender. Linear first-order regression was used to determine the significant factors (alpha=.05) in the prediction model for each color direction of the color of the maxillary central incisor. Age, gender, and color of the other craniofacial structures were studied as potential predictors. Final predictions in each color direction were based only on the statistically significant factors, and then the color differences between observed and predicted CIELAB values for the central incisors were calculated and summarized. RESULTS: The statistically significant predictors of age and gender accounted for 36% of the total variability in L*. The statistically significant predictor of age accounted for 16% of the total variability in a*. The statistically significant predictors of age and gender accounted for 21% of the variability in b*. The mean Delta (SD) between predicted and observed CIELAB values for the central incisor was 5.8 (3.2). CONCLUSIONS: Age and gender were found to be statistically significant determinants in predicting the natural color of central incisors. Although the precision of these predictions was less than the median color difference found for all pairs of teeth studied, and may be considered an acceptable precision, further study is needed to reduce this precision to the limit of detection.
Authors: Enric Soler; Fernando Duran-Sindreu; Juan Basilio; Miguel Roig; Stefano Ardu; Juan R Mayoral Journal: Clin Oral Investig Date: 2017-01-05 Impact factor: 3.573
Authors: Suneel V Vadavadagi; K V Halini Kumari; Gopal Krishna Choudhury; Abhishek Madhukar Vilekar; Sitansu Sekhar Das; Debkant Jena; Bharat Kataraki; Bhavana B L Journal: J Clin Diagn Res Date: 2016-02-01
Authors: Cristina Gómez Polo; Miguel Gómez Polo; Javier Montero; Juan Antonio Martínez Vazquez De Parga; Alicia Celemin Viñuela Journal: Int Dent J Date: 2015-07-27 Impact factor: 2.607