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Evaluation of a Novel Computer Color Matching System Based on the Improved Back‐Propagation Neural Network Model

PURPOSE
To explore the feasibility of a novel computer color-matching (CCM) system based on the improved back-propagation neural network (BPNN) model by comparing it with the traditional visual method.


MATERIAL AND METHODS
Forty-three metal-ceramic specimens were fabricated by proportionally mixing porcelain powders. Thirty-nine specimens were randomly selected to train the BPNN model, while the remaining four specimens were used to test and calibrate the model. A CCM system based on the improved BPNN model was constructed using MATLAB software. A comparison of the novel CCM system and the traditional visual method was conducted by evaluating the color reproduction results of 10 maxillary central incisors. Metal-ceramic specimens were fabricated using two color reproduction approaches. Color distributions (L*, a*, and b*) of the target teeth and of the corresponding metal-ceramic specimens were measured using a spectroradiometer. Color differences (ΔE) and color distributions (ΔL*, Δa*, and Δb*) between the teeth and their corresponding specimens were calculated.


RESULTS
The average ΔE value of the CCM system was 1.89 ± 0.75, which was lower than that of the visual approach (3.54 ± 1.11, p < 0.01). With respect to color distributions, substantial differences were found between the two color-matching systems, except for ΔL* (p > 0.05).


CONCLUSION
The novel CCM system produced greater accuracy in color reproduction within the given color space than the traditional visual approach.