Raman microspectroscopy/micro‐optical coherence tomography approach for chairside diagnosis of periodontal diseases: A pilot study
Bibliographic record
Abstract
BACKGROUND: Our objective was to develop and test a combined Raman microspectroscopy (RMS) and micro-optical coherence tomography (μOCT) approach for chairside quantification of gingival collagen, DNA, epithelium, and connective tissue. We hypothesized that a high-resolution RMS/μOCT can characterize healthy and inflamed periodontal tissues for diagnosis and disease activity monitoring. METHODS: A prototype instrument was developed, tested ex vivo on gingival specimens and optimized for in vivo intraoral use. The primary outcome measures were the ratios of oral epithelium to connective tissue thickness (OE:CT) and the amount of DNA to collagen type I (DNA/Col 1), and the thickness of sulcular epithelium (SE). For ex vivo testing, eight subjects with healthy periodontal tissues or with Stage II to IV periodontitis were included in the study and underwent crown-lengthening or periodontal surgical procedures, respectively. Gingival biopsies were scanned by RMS/μOCT and histometric analyses were performed. The proof-of-concept study included OE/CT, DNA/Col 1, and SE assessed in six volunteers with or without signs of gingival inflammation (n = 3/group). RESULTS: The spatially co-registered RMS spectra revealed opposing changes in the collagen and DNA peaks of inflamed compared with healthy tissues (P <0.05). Combined RMS/μOCT analysis showed that OE/CT, DNA/Col, and SE are significantly different between healthy and inflamed sites (P <0.05). Histological assessments confirmed the differences detected by RMS/μOCT. Qualitative analysis of DNA/Col 1 ratios indicated Col I content as the main distinguishing feature for health and DNA content for periodontitis. CONCLUSION: Results suggest that combined RMS/μOCT chairside imaging may distinguish between healthy and diseased sites by evaluating marginal periodontal morphological and biochemical features.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".