Revilla-León Marta, Gohil Aishwa, Barmak Abdul B, Zandinejad Amirali, Raigrodski Ariel J, Alonso Pérez-Barquero Jorge
Department of Restorative Dentistry, School of Dentistry, University of Washington, Seattle, WA.
Kois Center, Seattle, WA.
J Prosthodont. 2023 Apr;32(4):331-339. doi: 10.1111/jopr.13537. Epub 2022 Jun 6.
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.
测量最佳拟合（BF）算法（整个数据集、基于3个或6个点的地标法或基于截面的BF）对虚拟模型及其对齐差异的影响。
获取一个下颌模型牙并使用工业扫描仪（GOM Atos Q 3D 12M）进行数字化处理，获得一个对照网格。使用口腔内扫描仪（TRIOS 4）对模型牙进行数字化处理。根据对齐程序，创建四组：整个数据集的BF（BF组）、使用3个参考点的基于地标的BF（LBF-3组）、使用6个参考点的基于地标的BF（LBF-6组）以及基于截面的BF（SBF组）。计算均方根（RMS）误差。使用单因素方差分析和事后两两多重比较Tukey检验来分析数据（α = 0.05）。
各分组间RMS误差平均值存在显著差异（p < 0.001）。Tukey检验显示BF组与LBF-3组之间RMS误差平均值存在显著差异（p = 0.022），BF组与LBF-6组之间（p < 0.001），LB-3组与LB-6组之间（p < 0.001），LBF-3组与SBF组之间（p < 0.001），以及LBF-6组与SBF组之间（p < 0.001）。LBF-6组的准确性最低，而SBF组和BF组获得了最高的准确性值。此外，在测试的各分组间显示出显著的标准差差异（p < 0.001）。Tukey检验显示BF组与LBF-6组之间存在显著的标准差差异（p < 0.001），LBF-3组与LB-6组之间（p < 0.001），LBF-3组与SBF组之间（p = 0.004），以及LBF-6组与SBF组之间（p < 0.001）。BF组和SBF组显示出同等且最高的精度，而LBF-6组的精度最低。
所测试的最佳拟合算法影响虚拟模型的对齐差异。与基于地标的方法相比，整个数据集或基于截面的最佳拟合算法获得了最高的虚拟模型对齐准确性和精度。
