Dysphagia assessment based on photoacoustic imaging: a pilot ex vivo and in vivo study in infant swine models
Bibliographic record
Abstract
Swallowing impairments, such as dysphagia, pose significant health risks, including aspiration pneumonia, especially in vulnerable populations like infants and the elderly. Traditional diagnostic methods like videofluoroscopy and Fiberoptic Endoscopic Evaluation of Swallowing have limitations, including radiation exposure and discomfort. This study explores the potential of photoacoustic imaging as a non-invasive alternative for detecting swallowing events. Utilizing a 10 mg/mL charcoal solution as a contrast agent, we conducted both ex-vivo and in-vivo experiments using pig models. The ex-vivo tests on pig cadavers validated the system's ability in detecting charcoal flow in the airway. Subsequent in-vivo experiments on live pigs, conducted with synchronized videofluoroscopy, demonstrated photoacoustic's potential in seeing the same structure as videofluoroscopy. Our preliminary investigation indicates that photoacoustic imaging could offer a safer, more accurate method for dysphagia assessment, particularly in pediatric settings. Supplementary Information: The online version contains supplementary material available at 10.1007/s44258-025-00062-6.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".