Intraoperative handheld probe for 3D imaging of pediatric benign vocal fold lesions using optical coherence tomography (Conference Presentation)
Bibliographic record
Abstract
Excessive and repetitive force applied on vocal fold tissue can induce benign vocal fold lesions. Children affected suffer from chronic hoarseness. In this instance, the vibratory ability of the folds, a complex layered microanatomy, becomes impaired. Histological findings have shown that lesions produce a remodeling of sup-epithelial vocal fold layers. However, our understanding of lesion features and development is still limited. Indeed, conventional imaging techniques do not allow a non-invasive assessment of sub-epithelial integrity of the vocal fold. Furthermore, it remains challenging to differentiate these sub-epithelial lesions (such as bilateral nodules, polyps and cysts) from a clinical perspective, as their outer surfaces are relatively similar. As treatment strategy differs for each lesion type, it is critical to efficiently differentiate sub-epithelial alterations involved in benign lesions. In this study, we developed an optical coherence tomography (OCT) based handheld probe suitable for pediatric laryngological imaging. The probe allows for rapid three-dimensional imaging of vocal fold lesions. The system is adapted to allow for high-resolution intra-operative imaging. We imaged 20 patients undergoing direct laryngoscopy during which we looked at different benign pediatric pathologies such as bilateral nodules, cysts and laryngeal papillomatosis and compared them to healthy tissue. We qualitatively and quantitatively characterized laryngeal pathologies and demonstrated the added advantage of using 3D OCT imaging for lesion discrimination and margin assessment. OCT evaluation of the integrity of the vocal cord could yield to a better pediatric management of laryngeal diseases.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".