Optical Coherence Tomography for Clinical Otology
Bibliographic record
Abstract
The potential utility of OCT for diagnostics in otology has been acknowledged for nearly two decades, but studies have focused largely on application to understanding basic physiology where animals and cadavers can be used, or where only the tympanic membrane is of interest. We performed benchtop experiments in human cadavers with a custom OCT system to understand the design challenges and practicalities of moving towards its application to live, awake subjects with real-world pathologies of the middle ear. We quantify the deleterious effects of imaging the middle ear volume through the intact tympanic membrane, and demonstrate new clinical applications of imaging erosions of the osscicles and post-operative ossicular prosthesis tracking. New swept-laser technology enabled fast and phase-stable OCT measurements that capture both structure and displacement simultaneously in a technique called OCT-DV, the functionality of which was integrated into a mounted microscope suitable for use in live humans. The system allowed viewing of the full lateral and axial extents of the tympanic membrane and middle ear cavity, and was used to perform the first in vivo OCT-DV measurements in live humans, notably at the incus through the intact tympanic membrane. Special effort was made to make the system provide immediate, real-time results to the operator for maximum usability, and relied on GPU acceleration of OCT-DV. The same system was applied to cohorts of individuals with normal hearing (N=42 ears), and of individuals clinically diagnosed with otosclerotic stapes fixation (N=13 ears). We show that the OCT-DV implementation in our instrument was able to discriminate between the two samples with particularly good sensitivity (1.00) and specificity (0.98) using absolute peak-to-peak displacement measured at the incus at a stimulus frequency of 500Hz, and that there exist some technical improvements that could better separate the two groups. Specifically, addressing the practical displacement sensitivity penalty incurred in imaging live, awake subjects. We also detail progress on continued development of the system, and present several unique case studies where OCT and OCT-DV can offer additional insight into the state of the middle ear.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 0.021 |
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".