Application of a registration method on magnetic resonance images to evaluate the displacement field of a human subject ear canal due to various earplug insertions
Bibliographic record
Abstract
Earplugs are a usual way to protect workers subjected to noise exposure. However, the efficiency of these hearing protection devices is often affected by induced discomforts. A factor suspected to impact both acoustical and physiological comfort attributes of earplugs is the deformation they apply on the ear canal walls. As the geometry of both open and occluded ear canal is difficult to obtain, the ear canal deformation due to earplug insertion is not trivial to evaluate. Current medical imaging techniques and image post-processing methods are promising tools to investigate this deformation. In a previous study of the authors, an approach using registration methods on medical images had been proposed to estimate the ear canal displacement field induced by earplug insertion. This approach had been validated in the case of computed tomography scans of a human-like artificial ear occluded by a controlled-shape custom molded earplug. In the present study, this approach is used to evaluate the ear canal displacement of a human subject for various earplug insertions (foam, pre-molded and custom made) in the case of magnetic resonance images. The computed displacement field shows noteworthy differences between each earplug and gives information on how and where the occluded ear canal deforms.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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".