Estimating Vocal Fold Contact Pressure from Raw Laryngeal High-Speed Videoendoscopy Using a Hertz Contact Model
Bibliographic record
Abstract
The development of trauma-induced lesions of the vocal folds (VFs) has been linked to a high collision pressure on the VF surface. However, there are no direct methods for the clinical assessment of VF collision, thus limiting the objective assessment of these disorders. In this study, we develop a video processing technique to directly quantify the mechanical impact of the VFs using solely laryngeal kinematic data. The technique is based on an edge tracking framework that estimates the kinematic sequence of each VF edge with a Kalman filter approach and a Hertzian impact model to predict the contact force during the collision. The proposed formulation overcomes several limitations of prior efforts since it uses a more relevant VF contact geometry, it does not require calibrated physical dimensions, it is normalized by the tissue properties, and it applies a correction factor for using a superior view only. The proposed approach is validated against numerical models, silicone vocal fold models, and prior studies. A case study with high-speed videoendoscopy recordings provides initial insights between the sound pressure level and contact pressure. Thus, the proposed method has a high potential in clinical practice and could also be adapted to operate with laryngeal stroboscopic systems.
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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".