Fusion machine learning model predicts CAD-CAM ceramic colors and the corresponding minimal thicknesses over various clinical backgrounds.

This study has developed and optimized a machine learning model to accurately predict the final colors of CAD-CAM ceramics and determine their required minimum thicknesses to cover different clinical backgrounds. A total of 120 ceramic specimens (2 mm, 1 mm and 0.5 mm thickness; n = 10) of four CAD-CAM ceramics - IPS e.max, IPS ZirCAD, Upcera Li CAD and Upcera TT CAD - were studied. The CIELab coordinates (L*, a* and b*) of each specimen were obtained over seven different clinical backgrounds (A1, A2, A3.5, ND2, ND7, cobalt-chromium alloy (CC) and medium precious alloy (MPA)) using a digital spectrophotometer. The color difference (ΔE) and lightness difference (ΔL) results were submitted to 39 different models. The prediction results from the top-performing models were used to develop a fusion model via the Stacking integrated learning method for best-fitting prediction. The SHapley Additive exPlanation (SHAP) was performed to interpret the feature importance. The fusion model, which combined the ExtraTreesRegressor (ET) and XGBRegressor (XGB) models, demonstrated minimal prediction errors (R2 = 0.9) in the external testing sets. Among the investigated variables, thickness and background colors (CC and MPA) majorly influenced the final color of restoration. To achieve perfect aesthetic restoration (ΔE<2.6), at least 1.9 mm IPS ZirCAD or 1.6 mm Upcera TT CAD were required to cover the CC background, while two tested glass-ceramics did not meet the requirements even with thicknesses over 2 mm. The fusion model provided a promising tool for automate decision-making in material selection with minimal thickness over various clinical background. [Display omitted] • Fusion Machine Learning (ML) model accurately predicts the final color of CAD-CAM restoration. • Ceramic thickness is the most influential factor in predicting the desired color outcome. • ML model assists in material selection and determining optimal restoration thickness over various clinical backgrounds. [ABSTRACT FROM AUTHOR]