High Frequency Ex Vivo Ultrasound Imaging of the Middle Ear to Show Simulated Ossicular Pathology
Bibliographic record
Abstract
HYPOTHESIS: To illustrate the ability of high frequency ultrasound (HFUS) using a transducer array to demonstrate a variety of simulated clinical scenarios involving the ossicular chain. BACKGROUND: HFUS (>20 MHz) is a relatively new area of ultrasonic imaging that provides an order of magnitude better image resolution than the conventional low-frequency systems. HFUS may be a real-time imaging system that could be used in the clinic and would complement computed tomography (CT) and magnetic resonance imaging (MRI) to enhance the decision-making process for patients with middle ear pathology. METHODS: Using a commercially available HFUS scanner, we imaged a variety of simulated clinical scenarios to demonstrate the ability of HFUS to image middle ear pathology. RESULTS: We were able to clearly demonstrate real-time visualization of ossicular pathology in human temporal bones, whereas there are some limitations in the current technique to be addressed before it is used in vivo. CONCLUSION: HFUS allows excellent visualization of middle ear anatomy and pathology through an intact tympanic membrane (TM), and these experiments go some way towards giving the otologist access to high resolution, real-time imaging of the middle ear in the clinic.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".