High throughput image cytometry for detection of suspicious lesions in the oral cavity
Bibliographic record
Abstract
The successful management of oral cancer depends upon early detection, which relies heavily on the clinician's ability to discriminate sometimes subtle alterations of the infrequent premalignant lesions from the more common reactive and inflammatory conditions in the oral mucosa. Even among experienced oral specialists this can be challenging, particularly when using new wide field-of-view direct fluorescence visualization devices clinically introduced for the recognition of at-risk tissue. The objective of this study is to examine if quantitative cytometric analysis of oral brushing samples could facilitate the assessment of the risk of visually ambiguous lesions. About 369 cytological samples were collected and analyzed: (1) 148 samples from pathology-proven sites of SCC, carcinoma in situ or severe dysplasia; (2) 77 samples from sites with inflammation, infection, or trauma, and (3) 144 samples from normal sites. These were randomly separated into training and test sets. The best algorithm correctly recognized 92.5% of the normal samples, 89.4% of the abnormal samples, 86.2% of the confounders in the training set as well as 100% of the normal samples, and 94.4% of the abnormal samples in the test set. These data suggest that quantitative cytology could reduce by more than 85% the number of visually suspect lesions requiring further assessment by biopsy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".