Use of simulated data to explore the application of optical coherence tomography for classifying middle-ear pathologies
Bibliographic record
Abstract
Optical coherence tomography (OCT) vibrometry is a non-invasive tool for functional imaging of the middle ear. It provides spatially resolved vibrational responses and also anatomical images of the same ear. Our objective here was to explore the potential of OCT vibration measurements at the incus, as well as at the umbo, to distinguish among middle-ear disorders. Our approach was to build finite-element models of normal and pathological ears, generate large amounts of synthetic data, and then classify the simulated data into normal and pathological groups using a decision tree based on features extracted from simulated vibration magnitudes. We could distinguish between normal ears and ears with incudomallear joint (IMJ) disarticulation or stapes fixation, with the sensitivity and specificity both being 1.0; distinguish between stapes fixation and IMJ disarticulation with a sensitivity of 0.900 and a specificity of 0.889; and distinguish ears with ISJ disarticulation from normal ears with a sensitivity of 0.784 and a specificity of 0.872. Less extreme pathologies were also simulated. The results suggest that the vibration measurements within the middle ear that can be provided by OCT (e.g., at the incus) may be very valuable for diagnosis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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".