Standardisation of an AI-based vocal fold assessment tool on a recurrent respiratory papillomatosis model
Bibliographic record
Abstract
Objective: The assessment of extension of papilloma growth in recurrent respiratory papillomatosis (RRP) on vocal folds can be performed quantitatively utilising artificial intelligence (AI). Methods: This study evaluated the efficacy of an AI-based annotation system, Glottis Coverage - Artificial Intelligence and Deep learning (GC-AID) in 4 patients to assess affected mucosa in white light (WL) and narrow band imaging modalities as a case-study for future applications. Results: In healthy larynges, the mean difference between areas of the right and left vocal folds was minimal (2.6%). For patient # 4, following treatment, RRP coverage in WL decreased from 69.5% to 42.6%. A similar improvement was observed for patient # 1, while no significant benefits were noted for patients # 2 and # 3. Conclusions: The extent of RRP was precisely measured with GC-AID before and after treatment. Obtaining objective, quantitative results was possible with frame extraction and annotation using the system described herein.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".