Scanning Accuracy of 10 Intraoral Scanners for Single-crown and Three-unit Fixed Denture Preparations: An In Vitro Study.
Bibliographic record
Abstract
OBJECTIVE: To evaluate the accuracy of 10 intraoral scanners for single-crown and three-unit preparation models. METHODS: A maxillary partially edentulous model was fabricated. A dental cast scanner was used to obtain standard tessellation language (STL) data. Ten intraoral scanners, namely Trios 2 (TR2; 3Shape, Copenhagen, Denmark), True Definition (TD; 3M, Saint Paul, MN, USA), CEREC AC Omnicam (OM; Dentsply Sirona, Charlotte, NC, USA), Organical Scan Oral (OS; R+K, Berlin, Germany), PlanScan (PS; Planmeca, Helsinki, Finland), DWIOP (DW; Dental Wings, Montreal, Canada), Xianlin (XL; Hangzhou Xianlin, Hangzhou, China), DL-100 (DL; Guangzhou Longcheng, Guangzhou, China), Trios 3 (TR3; 3Shape) and i500 (MD; MEDIT, Seoul, South Korea) were used to obtain stereolithography data as test groups. Trueness, precision and surface accuracy were evaluated by deviation analysis using 3D image processing software. One tooth with a three-unit preparation for each test group was registered with the reference scan data, and the absolute distance from another tooth was calculated as the absolute accuracy. The data were analysed using a Mann-Whitney U test and Dunn-Bonferroni test (α = 0.05). RESULTS: The best trueness, precision and surface accuracy of scanning single crown preparation were recorded with TD (trueness 2.9 μm and precision 1.9 μm) and XL (surface accuracy 20.3 ± 2.9 μm). The best trueness, precision, surface accuracy and absolute accuracy of three-unit preparations were recorded with TD (2.6 μm), XL (1.9 μm), OM (27.1 ± 5.2 μm) and TR3 (79.2 ± 19.6 μm), respectively. There was no statistically significant difference in trueness between single- and multiple-unit preparations for any of the intraoral scanners (P > 0.05). A statistically significant difference in the surface accuracy between single and multiple preparations was found for TR2, TD, OM, DW, XL, DL and MD (P < 0.05). CONCLUSION: The trueness and precision of intraoral scanners for scanning three-unit preparations were nearly the same as those for single-crown preparations; however, with the exception of OS, PS and TR3, the surface accuracy of single-crown preparations was significantly better than that for three-unit preparations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".