Detection and diagnosis of oral neoplasia with an optical coherence microscope
Bibliographic record
Abstract
The use of high resolution, in vivo optical imaging may offer a clinically useful adjunct to standard histopathologic techniques. A pilot study was performed to investigate the diagnostic capabilities of optical coherence microscopy (OCM) to discriminate between normal and abnormal oral tissue. Our objective is to determine whether OCM, a technique combining the subcellular resolution of confocal microscopy with the coherence gating and heterodyne detection of optical coherence tomography, has the same ability as confocal microscopy to detect morphological changes present in precancers of the epithelium while providing superior penetration depths. We report our results using OCM to characterize the features of normal and neoplastic oral mucosa excised from 13 subjects. Specifically, we use optical coherence and confocal microscopic images obtained from human oral biopsy specimens at various depths from the mucosal surface to examine the optical properties that distinguish normal and neoplastic oral mucosa. An analysis of penetration depths achieved by the OCM and its associated confocal arm found that the OCM consistently imaged more deeply. Extraction of scattering coefficients from reflected nuclear intensity is successful in nonhyperkeratotic layers and shows differentiation between scattering properties of normal and dysplastic epithelium and invasive cancer.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".