Fusion of middle ear optical coherence tomography and computed tomography in three ears
Bibliographic record
Abstract
Abstract Importance Middle ear OCT imaging in patients has not previously been directly compared to a standard of care clinical 3D imaging technology such as CT. This represents the first such comparison and provides new insight into OCT’s capabilities, strengths and limitations. Objective To qualitatively compare the capabilities of middle ear OCT to CT in normal and pathological ears on representative slices in co-registered OCT and CT datasets. Design, Setting, and Participants One normal middle ear, one ear affected by traumatic injury and one ear with cholesteatoma 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 co-registration with manual landmark selection. Main Outcomes and Measures Images were analyzed qualitatively for field of view, resolution, shadowing, artefacts, soft tissue and bony tissue contrast and presentation of diagnostically important features. Results In the three imaged ears, OCT was capable of visualizing many of the important features indicative of middle ear pathology. When compared to CT, OCT was found to exhibit a limited field of view (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 co-registered with CT images. Conclusions and Relevance The results support a role for middle ear OCT in otological diagnostics. While OCT is not capable of replacing CT due to its limited FOV and inability to image through thick bony tissues, it can visualize many 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 to CT, be an appealing imaging modality for many applications in middle ear diagnostics. Key Points Question (1 sentence) What are the strengths and limitations of middle ear optical coherence tomography (OCT) imaging compared to computed tomography (CT) in real-world clinical imaging scenarios? Finding (1-2 sentence) OCT and CT imaging produce complementary diagnostic information with CT offering unobstructed images of bony anatomy and OCT providing the ability to visualize soft tissue. Meaning (1 sentence) OCT, CT and fused OCT/CT imaging can each provide useful, complementary diagnostic information in clinical otology.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".