Algorithm to Predict the Final Color of Leucite-Reinforced Ceramic Restorations. (2022) - Kose, C.; Oliveira, D.; Roulet, J.F.; Pereira, P.; Rocha, M.G.
Abstract : Purpose/Aim: The aim of this study was to develop an algorithm to predict the final color of leucite-reinforced glass-based ceramic veneer restorations based on the substrate shade, ceramic shade, thickness, and translucency. Materials and Methods: Leucite-reinforced glass-ceramics (IPS Empress CAD) in 4 different shades (A1 HT, A3 HT, A1 LT and A3 LT) were sectioned in thicknesses of 0.3, 0.5, 0.7 and 1.2 mm using a precision saw machine. The CIELab coordinates of each specimen were obtained over four different backgrounds (black, white, A1 and A3) interposed with an experimental translucent resin-based cement with a refractive index of 1.5229 and without photoinitiators to reproduce the optical properties of the ceramic-cement and cement-substrate interfaces. A calibrated spectrophotometer (CMD-700, Konica Minolta) with a D65 illuminant and target mask sensor-opening diameter of 3 mm was used for the data collection. The color change (ΔE00, CIEDE2000) values and all the CIELab values for each of the experimental groups were submitted to a multivariate linear regression. The regression model was adjusted according to the weights of each dependent variable to achieve the model best-fitting. All functions were programmed into an excel spreadsheet to execute the algorithm automation to the predicted final shade of the ceramic restoration. Results: Different substrates, ceramic shades, and thicknesses influenced the L*, a* and b* of the final restoration. From all variables, the substrate influences the final ceramic shade more, followed by the ceramic thickness and the L*, a* and b* of the ceramic. It was found that the thickness is influence by the sigmoid function of the variable ΔE00 of the substrate vs. ceramic colors (Adjusted-R2 = 0.915, p = 0.014). The mathematical models were able to generate functions to predict the shade of the ceramic restoration according to the substrate shade, ceramic shade and thickness. Conclusions: Different substrates, ceramic shades, and ceramic thicknesses influence the L*, a* and b* coordinates of the final restoration. From all variables, the substrate influences the final ceramic shade the most, followed by the L*, a* and b* coordinates of the ceramic and the ceramic thickness. The influence of the ceramic thickness on the final color depends on the difference between ΔE00 of the substrate and the ceramic. The algorithm developed in the study can predict the final colors of the ceramic restorations made with Empress CAD HT and LT, based on the color of the substrate and different thicknesses.