Fusion of Middle Ear Optical Coherence Tomography and Computed Tomography
Bibliographic record
Abstract
Importance: Middle ear optical coherence tomography (OCT) imaging in patients has not previously been directly compared with a standard of care clinical 3-dimensional imaging technology, such as computed tomography (CT). Objective: To qualitatively compare the capabilities of middle ear OCT with CT in normal and pathological ears on representative slices in coregistered OCT and CT datasets. Design, Setting, and Participants: This case series included 3 patients and 3 ears: 1 normal middle ear, 1 ear affected by traumatic injury, and 1 ear with cholesteatoma. The ears were imaged with both OCT and high-resolution clinical temporal bone CT. Participants were drawn from the patient population of a tertiary otology clinic. CT and OCT images were aligned using rigid coregistration with manual landmark selection. Data were collected from January 2022 to April 2023, and data were analyzed from February 2022 to December 2023. Main Outcomes and Measures: Images were analyzed qualitatively for field of view (FOV), resolution, shadowing, artifacts, soft tissue and bony tissue contrast, and presentation of diagnostically important features. Results: In the 3 imaged ears, OCT was capable of visualizing many of the important features indicative of middle ear pathology. Compared with CT, OCT exhibited a limited FOV largely confined to the mesotympanum and subject to shadowing from bony structures. However, OCT could resolve soft tissue features that were not readily apparent in the CT images to have a higher resolution than CT and to provide excellent anatomical fidelity with CT, which allowed OCT images to be accurately coregistered with CT images. Conclusions and Relevance: In this case series, while OCT was not capable of replacing CT due to its limited FOV and inability to image through thick bony tissues, it visualized signs of pathology, including some soft tissue features, that are difficult to visualize with CT. Given OCT's ability to image in real time, its compatibility with in-office imaging, and its lack of ionizing radiation, it may, despite its limitations compared with CT, be an appealing imaging modality for many applications in middle ear diagnostics.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".