Accuracy of 3D white light scanning of abutment teeth impressions: evaluation of trueness and precision
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초록
PURPOSE. This study aimed to evaluate the accuracy of digitizing dental impressions of abutment teeth using a white light scanner and to compare the findings among teeth types. MATERIALS AND METHODS. To assess precision, impressions of the canine, premolar, and molar prepared to receive all-ceramic crowns were repeatedly scanned to obtain five sets of 3-D data (STL files). Point clouds were compared and error sizes were measured (n=10 per type). Next, to evaluate trueness, impressions of teeth were rotated by 10°–20° and scanned. The obtained data were compared with the first set of data for precision assessment, and the error sizes were measured (n=5 per type). The Kruskal–Wallis test was performed to evaluate precision and trueness among three teeth types, and post-hoc comparisons were performed using the Mann–Whitney U test with Bonferroni correction (α=.05). RESULTS. Precision discrepancies for the canine, premolar, and molar were 3.7 μm, 3.2 μm, and 7.3 μm, respectively, indicating the poorest precision for the molar (P<.001). Trueness discrepancies for teeth types were 6.2 μm, 11.2 μm, and 21.8 μm, respectively, indicating the poorest trueness for the molar (P=.007). CONCLUSION. In respect to accuracy the molar showed the largest discrepancies compared with the canine and premolar. Digitizing of dental impressions of abutment teeth using a white light scanner was assessed to be a highly accurate method and provided discrepancy values in a clinically acceptable range. Further study is needed to improve digitizing performance of white light scanning in axial wall.
Abstract
PURPOSE. This study aimed to evaluate the accuracy of digitizing dental impressions of abutment teeth using a white light scanner and to compare the findings among teeth types. MATERIALS AND METHODS. To assess precision, impressions of the canine, premolar, and molar prepared to receive all-ceramic crowns were repeatedly scanned to obtain five sets of 3-D data (STL files). Point clouds were compared and error sizes were measured (n=10 per type). Next, to evaluate trueness, impressions of teeth were rotated by 10°–20° and scanned. The obtained data were compared with the first set of data for precision assessment, and the error sizes were measured (n=5 per type). The Kruskal–Wallis test was performed to evaluate precision and trueness among three teeth types, and post-hoc comparisons were performed using the Mann–Whitney U test with Bonferroni correction (α=.05). RESULTS. Precision discrepancies for the canine, premolar, and molar were 3.7 μm, 3.2 μm, and 7.3 μm, respectively, indicating the poorest precision for the molar (P<.001). Trueness discrepancies for teeth types were 6.2 μm, 11.2 μm, and 21.8 μm, respectively, indicating the poorest trueness for the molar (P=.007). CONCLUSION. In respect to accuracy the molar showed the largest discrepancies compared with the canine and premolar. Digitizing of dental impressions of abutment teeth using a white light scanner was assessed to be a highly accurate method and provided discrepancy values in a clinically acceptable range. Further study is needed to improve digitizing performance of white light scanning in axial wall.
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