Comparative Evaluation of Machine Learning Algorithms for Spectrophotometric Dental Shade Classification

Accurate dental shade matching is critical for achieving optimal esthetic outcomes in restorative dentistry. This study presents a comparative evaluation of machine learning algorithms for spectrophotometric dental shade classification, focusing on Support Vector Machine, Random Forest, and Extreme Learning Machine approaches. Spectral reflectance data from 1,280 standardized dental composite specimens (as controlled surrogates for shade-guide categories) across 16 VITA Classical shades were collected using a calibrated spectrophotometer. Feature extraction methods, including CIELAB coordinates, spectral coefficients, and principal component analysis, were systematically compared. Experimental results demonstrate that the Extreme Learning Machine achieved the highest classification accuracy of 97.8%; its mean ΔE00 was 1.42, and 89.3% of predictions fell below the clinical acceptability threshold of ΔE00 = 1.8, with a b coordinate RMSE of 2.14. Random Forest demonstrated superior robustness in edge-shade classification, achieving 94.2% accuracy. The findings provide practical guidance for selecting algorithms in industrial dental shade-matching applications.