NEW METHOD USING IMAGE ANALYSIS TO MEASURE GINGIVAL COLOR
Bibliographic record
Abstract
For many years, observation of gingival color has been a popular area of dental research. However these methods are hard to analyze for any other than the different base conditions and colors. Thus we introduced an alternative method using image analysis to measure gingival color. For the research we performed a dental examination on 30 female students. The system is set up by aligning the camera area and facial area. The subject's chin is placed in a fixed chin cup mounted 30 cm from the camera lens. Each image is acquired such that comparison may be made with the original bite holder as well as a standard color scale. After converted to computer we used a curves dialog box for color adjustment. The curves dialog box allows adjustment of the entire tonal range of an image. The results of the analysis of the free gingiva compared to the attached gingiva are that attached gingiva was more vivid red and yellow compared to the free gingiva. In conclusion, the system described herein of digital caputre and comparison of color images, analysis and separation in three channels of free and attached ginigval surface images and matching with colorimetric scales may be useful for demonstrating the diversity of ginigval color as well as analysis of gingival health.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".