Influence of scanbody design and intraoral scanner on the trueness of complete arch implant digital impressions: An in vitro study

PloS one(2023)

Cited 0|Views3
No score
Abstract
This study aimed to compare the accuracy of full-arch digital implant impressions using seven different scanbodies and four intraoral scanners. A 3D-printed maxillary model with six implants and their respective multi-unit abutments was used for this study. Seven scanbodies (SB1, SB2, SB3, SB4, SB5, SB6, and SB7) and four intraoral scanners (Primescan (R), Omnican (R), Trios 3 (R), and Trios 4 (R)) were assessed. Each combination group was scanned ten times and a dental lab scanner (D2000, 3Shape) was used as a reference. All scans were exported as STL files, imported into Convince software (3Shape) for alignment, and later into Blender software, where their 3D positions were analyzed using a Python script. The 3D deviation, angular deviation, and linear distance between implants #3 and #14 were also measured. Accuracy was measured in terms of "trueness" (scanbody 3D deviation between intraoral scan and desktop scan). Kruskal-Wallis followed by the Bonferroni correction was used to analyze the data (alpha = .05). The study found statistically significant differences in digital impression accuracy among the scanners and scanbodies (p<0.001). When comparing different intraoral scanners, the Primescan system showed the smallest 3D deviation (median 110.59 mu m) and differed statistically from the others, while Trios 4 (median 122.35 mu m) and Trios 3 (median 130.62 mu m) did not differ from each other (p = .284). No differences were found in the linear distance between implants #3 and #14 between Trios 4, Primescan, and Trios 3 systems. When comparing different scanbodies, the lowest median values for 3D deviation were obtained by SB2 (72.27 mu m) and SB7 (93.31 mu m), and they did not differ from each other (p = .116). The implant scanbody and intraoral scanner influenced the accuracy of digital impressions on completely edentulous arches.
More
Translated text
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined