Semi-automatic assessment of hyoid bone motion in digital videofluoroscopic images
Bibliographic record
Abstract
The swallowing process involves triggering the movements of a number of muscles in the throat that transports the food from the mouth to the stomach successfully and at the same time prevents it from getting into the airway and the lung. In order to detect abnormalities in the swallowing process, radiologists use a technique called videofluoroscopic swallowing study. It is a video of X-ray images that are taken while the patient swallows food, which is later visually inspected by the radiologist to evaluate the patient's swallowing ability. It has been reported that measuring the movement of the hyoid bone plays an important role in the evaluation process. However, due to the subjective nature of visual inspection, radiologists have difficulty reaching unanimous decision about the outcome of the evaluation. In this research, a semi-automatic method is proposed which tracks the hyoid bone and quantifies its movement. Using a classification-based approach, the proposed method automatically identifies the region of interest before identifying the hyoid bone. This allows limiting image processing procedures to the relevant area in the image. Results show that the proposed method identifies and tracks the hyoid bone with significant accuracy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| 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 | 0.000 |
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".