Comparison of Accuracy of Current Ten Intraoral Scanners
Bibliographic record
Abstract
There have been various developments in intraoral 3D scanning technology. This study is aimed at investigating the accuracy of 10 scanners developed from 2015 to 2020. A maxillary dental model with reference points was printed from Form 2 (FormLabs, Somerville, MA, USA). The model was scanned 5 times with each intraoral scanner (IOS); Trios 3 (normal and high‐resolution mode); Trios 4 (normal and high‐resolution mode) (3Shape Trios A/S, Copenhagen, Denmark); iTero Element, iTero 2, and iTero 5D Element (Align Technologies, San Jose, California, USA); Dental Wings (Dental Wings, Montreal QC, Canada); Panda 2 (Pengtum Technologies, Shanghai, China); Medit i500 (Medit Corp. Seoul, South Korea); Planmeca Emerald™ (Planmeca, Helsinki, Finland); and Aoralscan (Shining 3D Tech. Co., Ltd., Hangzhou, China). After the scan, the 3D scanned stereolithography files were created. The various distances were measured five times in X , Y , Z , and X Y axes of various scans and with a vernier caliper (control) and from the Rhinoceros software. The data were analyzed using SPSS 18. Test for the normality of the various measurement data were done using Kolmogorov‐Smirnov test. The trueness and precision of the measurements were compared among the various scans using the Kruskal‐Wallis test. The significance was considered at P < 0.05. The trueness of the intraoral scans was analyzed by comparing the measurements from the control. Precision was tested through the measurements of repeated scans. It showed that more the distance is less the accuracy for all scanners. In all studied scanners, the trueness varied but precision was favorably similar. Diagonal scanning showed less accuracy for all the scanners. Hence, when scanning the full arch, the dentist needs to take more caution and good scan pattern. Trios series showed the best scan results compared to other scanners.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".