Machine-Learning-Based Spectral Modeling: A Biomimetic Guide for Enhancing Esthetics.

Objective: To provide guidelines and means for optimal coverage and distribution of computer models with 1-10 clusters, designed based on an in vivo extensive dental colorimetric database and compare the findings with some reputable shade guides.

Materials and methods: The Fuzzy C-Means (FCM) algorithm was used to optimize the tab position in the CIELAB color space, while MATLAB Fuzzy Logic was used to implement the codes. A minimization process was performed to fine-tune the cluster centers, minimizing Coverage Error (CE₀₀) and Maximum Error (ME₀₀). Spectral reconstruction was performed, and the results were compared with the corresponding data for VITA classical A1-D4 (VC), Linearguide 3D-Master (LG), and Bleachedguide 3D-Master (BG) shade guide. Paired t-test was employed to assess the significance of CE₀₀ differences.

Results: CE₀₀ and ME₀₀ ranges for 1-to-10-tab models were 3.8 (14.2) to 1.7 (4.3), while the corresponding values for shade guides were 3.1 (9.1) for VC, 2.3 (6.0) for LG, and 2.9 (9.3) for BG. Hence, the 4-tab-model with CE₀₀ of 2.2 and ME₀₀ of 7.1, outperformed CE₀₀ of LG (the lowest/best CE₀₀ among shade guides), while the 6-tab-model, with CE₀₀ of 2.0 and ME₀₀ of 5.6, outperformed its ME₀₀. A paired t-test confirmed that increasing the number of tabs models resulted in significant CE improvement in all cases (p ≤ 0.05).

Conclusions: Shade guide models with only 4 to 6 tabs, optimized using computational techniques, outperformed physical dental shade guides tested, exhibiting significantly lower (better) Coverage Error and Maximum Error.

Clinical significance: Given that the spectral modeling was performed on an extensive in vivo database of natural teeth, this approach was validated as a biomimetic guide for shade guides and corresponding materials of the future, offering a possibility for better shade matching with fewer shades/shade tabs.