Factors influencing optical 3D scanning of vinyl polysiloxane impression materials
Bibliographic record
Abstract
PURPOSE: Future growth in dental practice lies in digital imaging enhancing many chairside procedures and functions. This revolution requires the fast, accurate, and 3D digitizing of clinical records. One such clinical record is the chairside impression. This study investigated how surface angle and surface roughness affect the digitizing of vinyl polysiloxane impression materials. MATERIALS AND METHODS: Seventeen vinyl polysiloxane impression materials were digitized with a white light optical digitizing system. Each sample was digitized at 3 different angles: 0 degrees, 22.5 degrees, and 45 degrees, and 2 digitizer camera f-stops. The digitized images were rendered on a computer monitor using custom software developed under NIH/NIDCR grant DE12225. All the 3D images were rotated to the 0 degrees position, cropped using Corel Photo-Paint 8 (Corel Corp, Ottawa, Ontario, Canada), then saved in the TIFF file format. The impression material area that was successfully digitized was calculated as a percentage of the total sample area, using Optimas 5.22 image processing software (Media Cybernetics, LP, Silver Spring, MD). The dependent variable was a Performance Value calculated for each material by averaging the percentage of area that digitized over the 3 angles. New samples with smooth and rough surfaces were made using the 7 impression materials with the largest Performance Values. These samples were tested as before, but with the additional angle of 60 degrees. Silky-Rock die stone (Whip Mix Corp, Louisville, KY) was used as a control. RESULTS: The Performance Values for the 17 impression materials ranged from 0% to 100%. The Performance Values for the 7 best materials were equivalent to the control at f/11 out to a surface angle of 45 degrees; however, only Examix impression material (GC America Inc, Alsip, IL) was equivalent to the control at f/11/\16. At the 60 degrees surface angle with f/11/\16, the Performance Values were 0% for all the impression materials, whereas that for the control was 90%. The difference in the Performance Values for the smooth and rough surface textures was 7%, which was not significant. CONCLUSIONS: The digitizing performance of vinyl polysiloxane impression materials is highly material and surface angle-dependent and is significantly lower than the die stone control when angles to 60 degrees are included. It is affected to a lesser extent by surface texture.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".