New Imaging Modalities in Otology
Bibliographic record
Abstract
Despite steady improvements in cross-sectional imaging of the ear, current technologies still have limitations in terms of resolution, diagnosis, functional assessment and safety. In this chapter, state-of-the-art imaging techniques in current clinical practice are presented including cone-beam computerized tomography, non-echo planar imaging magnetic resonance imaging, imaging for labyrinthine hydrops and imaging of the central auditory pathways. Potential future imaging modalities are also presented, including optical coherence tomography (OCT) and high-frequency ultrasound (HFUS) of the ear. These experimental modalities offer new opportunities for the assessment of ear structure and function. For example, middle ear structures can be visualized through the tympanic membrane, basilar membrane vibrations can be assessed through the round window and the passage of cochlear implants can be assessed in decalcified cochlear. Functional assessment of the middle ear using Doppler techniques are also discussed, including measurement of tympanic membrane and middle ear vibration amplitudes, visualization of dynamic changes, such as tensor tympani movements and movement of the tympanic membrane with breathing. These new modalities currently have limitations that preclude mainstream clinical use. For example, OCT is limited by the optical scattering of the thickened tympanic membrane and HFUS needs a coupling medium such as gel or fluid from the transducer to the imaged structure although it can visualize through thicker tissues. Nevertheless, further development of these novel techniques may provide an enhanced ability to assess the ear in conjunction with current technologies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".