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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".