A Novel Methodology to Validate the Accuracy of Extraoral Dental Scanners and Digital Articulation Systems.
Bibliographic record
Abstract
PURPOSE: The aim of the current study is to develop a novel method to investigate the accuracy of 3D scanners and digital articulation systems. MATERIALS AND METHODS: An upper and a lower poured stone model were created by taking impression of fully dentate male (fifty years old) participant. Titanium spheres were added to the models to allow for an easily recognisable geometric shape for measurement after scanning and digital articulation. Measurements were obtained using a Coordinate Measuring Machine to record volumetric error, articulation error and clinical effect error. Three scanners were compared, including the Imetric 3D iScan d104i, Shining 3D AutoScan-DS100 and 3Shape D800, as well as their respective digital articulation software packages. Stoneglass Industries PDC digital articulation system was also applied to the Imetric scans for comparison with the CMM measurements. RESULTS: All the scans displayed low volumetric error (p⟩0.05), indicating that the scanners themselves had a minor contribution to the articulation and clinical effect errors. The PDC digital articulation system was found to deliver the lowest average errors, with good repeatability of results. CONCLUSION: The new measuring technique in the current study was able to assess the scanning and articulation accuracy of the four systems investigated. The PDC digital articulation system using Imetric scans was recommended as it displayed the lowest articulation error and clinical effect error with good repeatability. The low errors from the PDC system may have been due to its use of a 3D axis for alignment rather than the use of a best fit.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".