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

OBJECTIVES: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. METHODS: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. RESULTS: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. SIGNIFICANCE:The fusion model provided a promising tool for automate decision-making in material selection with minimal thickness over various clinical background.