Optical Coherence Tomography for Clinical Otology
Bibliographic record
Abstract
Optical Coherence Tomography (OCT) is an interferometric imaging technique used to produce high-resolution depth-resolved images in tissue. By probing tissue with light, tissue morphology can be determined from the characteristics of an interference pattern produced by any light that is backscattered by sub-surface structures. OCT can be thought of as the optical analog to ultrasound. The middle ear is a unique part of the human body that is well-suited to diagnostic imaging using OCT. The eardrum, located at the end of the external ear canal, drives the bony ossicular chain (malleus, incus, stapes) to conduct sound to the inner ear. The eardrum is thin (approx. 100-300 microns) and translucent and so is easily penetrated by infrared light. OCT provides a window into the middle ear that could allow diagnostic capabilities unlike other technologies currently available in otology. While the potential for otological OCT has been recognized for several years, it has yet to be adapted into a form suitable for clinical practice. OCT’s ability to measure both structure and physical dynamics using Doppler detection points towards a system with tremendous diagnostic capabilities in the middle ear, particularly in the diagnosis of conductive hearing losses. We demonstrate a real-time OCT imaging system designed specifically for use in clinical middle ear imaging. The system is a custom-built swept-source OCT system that makes use of an akinetic tunable laser (Insight Photonic Solutions, Inc.) and optics designed for imaging live patients. Real-time signal processing is achieved on a graphics-processing-unit (GPU), including simultaneous structural imaging and Doppler vibrometric functional imaging, and has been integrated into a GUI for use by clinicians. We present our system in its final design stages as we prepare to deploy it for clinical trials. Images acquired in cadaveric human temporal bones and human volunteers will be presented.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".