Optimization of Tooth Color and Shade Guide Design

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Abstract
Purpose: One critical prerequisite for dental shade guides is to match the color range and distribution of human teeth. The purpose of this study was to design computer models for dental shade guides and compare them with an existing shade guide. A targeted coverage error for a newly developed shade guide was ΔEab < 2 with a corresponding CIE2000 value. Materials and Methods: A total of 1064 teeth were evaluated in vivo using an intra‐oral spectrophotometer. Shade guide models were designed using different methods for representation of the data set, hierarchical clustering, and nonlinear constrained optimization. Coverage error was calculated for both CIELAB and CIE2000 values. Recorded values were compared with coverage error of Vitapan Classical (VC) shade guide. Wilcoxon signed‐rank test for paired samples and linear regression were used in statistical analysis. Results: Coverage error of VC was 4.1 (SD 1.8), ranging from 0.5 to 11.5 ΔEab. Group A shades had the best match for human teeth (43.9%) followed by Groups C (24.1%), B (20.4%), and D (11.7%) shades, respectively. CIELAB coverage error of the newly designed 24‐tab shade guide using clustering and optimization was 2.05 (0.95) and 1.96 (0.92), respectively. Corresponding CIE2000 coverage error values were 1.43 (0.68) and 1.40 (0.65), respectively. A significant difference between results obtained using clustering and optimization was determined. CIELAB color differences were greater, but highly correlated as compared with their CIE2000 counterparts (ΔE00= 0.64 ×ΔE76+ 0.13, r > 0.99). Discussion: This study demonstrated that, compared with existing shade guides, future shade guides can provide either (a) similar coverage of tooth color with fewer tabs, thus simplifying shade matching procedure, or (b) better coverage of tooth color with a similar number of tabs, in both cases increasing the chances of satisfactory matches and, consequently, better esthetics. Conclusions: Both clustering and optimization enabled better representation of tooth color as compared with an existing dental shade guide. Optimization outperformed clustering and is therefore recommended as a method of choice for representation of tooth color and designing of dental shade guides.

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