Best-Fit Algorithm Influences on Virtual Casts' Alignment Discrepancies

To measure the influence of best-fit (BF) algorithms (entire dataset, 3 or 6 points landmark-based, or section-based BF) on virtual casts and their alignment discrepancies.

A mandibular typodont was obtained and digitized by using an industrial scanner (GOM Atos Q 3D 12M). A control mesh was acquired. The typodont was digitized by using an intraoral scanner (TRIOS 4). Based on the alignment procedures, four groups were created: BF of the entire dataset (BF group), landmark-based BF using 3 reference points (LBF-3 group), or 6 reference points (LBF-6 group), and section-based BF (SBF group). The root mean square (RMS) error was calculated. One-way ANOVA and post hoc pairwise multi-comparison Tukey were used to analyze the data (α = 0.05).

Significant RMS error mean value differences were found across the groups (p < 0.001). Tukey test revealed significant RMS error mean value differences between the BF and LBF-3 groups (p = 0.022), BF and LBF-6 groups (p < 0.001), LB-3 and LB-6 groups (p < 0.001), LBF-3 and SBF groups (p < 0.001), and LBF-6 and SBF groups (p < 0.001). The LBF-6 group had the lowest trueness, while SBF and BF groups obtained the highest trueness values. Furthermore, significant SD differences were revealed across the groups tested (p < 0.001). Tukey test revealed significant SD differences between the BF and LBF-6 groups (p < 0.001), LBF-3 and LB-6 groups (p < 0.001), LBF-3 and SBF groups (p = 0.004), and LBF-6 and SBF groups (p < 0.001). The BF and SBF groups showed equal and highest precision, while the LBF-6 group had the lowest precision.

The best-fit algorithms tested influenced the virtual casts' alignment discrepancy. Entire dataset or section-based best-fit algorithms obtained the highest virtual casts' alignment trueness and precision compared with the landmark-based method.