Segmentation of process-related contaminations on two-piece abutments using pixel-based machine learning: a new quantification approach?

Abstract

A reference method for quantifying contaminations on two-piece abutments manufactured using CAD/CAM has not yet been established. In the present in vitro study, a pixel--based machine learning (ML) method for detecting contamination on customized two-piece abutments was investigated and embedded in a semiautomated quantification pipeline. Forty-nine CAD/CAM zirconia abutments were fabricated and bonded to a prefabricated titanium base. All samples were analyzed for contamination by scanning electron microscopy (SEM) imaging followed by pixel--based ML and thresholding (SW) for contamination detection; quantification was performed in the postprocessing pipeline. Wilcoxon signed-rank test and Bland-Altmann plot were applied to compare both methods. The contaminated area fraction was recorded as a percentage. There was no statistically significant difference between the percentages of contamination areas (median = 0.004) measured with ML (median = 0.008) and with SW (median = 0.012), asymptotic Wilcoxon test: P = 0.22. The Bland-Altmann plot demonstrated a mean difference of -0.006% (95% confidence interval [CI] from -0.011% to 0.0001%) with increased values from a contamination area fraction of > 0.03% for ML. Both segmentation methods showed comparable results in evaluating surface cleanliness; pixel-based ML is a promising assessment tool for detecting external contaminations on zirconia abutments. Further studies are required to investigate the clinical performance of this tool.