Gingival shade guides: Colorimetric and spectral modeling.

Abstract

To design colorimetric and spectral models of gingival shade guides that adequately represent the color of human gingiva. A previously compiled database on the spectral reflectance of healthy keratinized gingiva was used for optimization. Coverage Error (CE) and Maximal Error (ME) were optimized using CIELAB and CIEDE2000 color difference formulas. A two-phase process included an FCM algorithm and a nonlinear optimization. A t test was used to compare the performance of the different numbers of clusters/tabs in gingival shade guide models (α = .05). CIELAB CE and ME for shade guide models with 3 to 6 clusters ranged from 3.1 to 3.9 (P = .028 for 3 vs. 4; and P = .033 for 5 vs. 6 cluster/tab comparison), while the corresponding CIEDE2000 range was from 2.1 to 2.8 (P < .001 for 3 vs. 4 tabs; P < .025 for 4 vs. 5; and P = 0.029 for 5 vs. 6 tab comparisons). The percentage of data points exhibiting a CIELAB color difference lower than the acceptability threshold ranged from 48.7% to 71.4%, and from 52.9% to 82.4%. for CIEDE2000. An increase in the number of clusters in the gingival shade guide models was associated with a decrease in coverage error (better match) to human gingiva. Gingival shade guide models with only 4 tabs provided a CIELAB and CIEDE2000 coverage error lower than the acceptability threshold for gingival color. Spectral clustering of human gingiva was determined to be valid. CIEDE2000 color difference formula outperformed the CIELAB formula in the optimization process. Providing a shade guide model with a small number of tabs and a coverage error lower than the 50:50% acceptability threshold would be an optimal solution for shade matching in dentistry. However, no actual gingival or tooth shade guide complies with this. The clustering method, with optimization of both Coverage Error and Maximal Error and spectral clustering that enables more reliable color formulation of cluster representatives of shade guide models, represents an advance when it comes to computer modeling in dentistry.