Lung vasculature imaging using speckle variance optical coherence tomography
Bibliographic record
Abstract
Architectural changes in and remodeling of the bronchial and pulmonary vasculature are important pathways in diseases such as asthma, chronic obstructive pulmonary disease (COPD), and lung cancer. However, there is a lack of methods that can find and examine small bronchial vasculature in vivo. Structural lung airway imaging using optical coherence tomography (OCT) has previously been shown to be of great utility in examining bronchial lesions during lung cancer screening under the guidance of autofluorescence bronchoscopy. Using a fiber optic endoscopic OCT probe, we acquire OCT images from in vivo human subjects. The side-looking, circumferentially-scanning probe is inserted down the instrument channel of a standard bronchoscope and manually guided to the imaging location. Multiple images are collected with the probe spinning proximally at 100Hz. Due to friction, the distal end of the probe does not spin perfectly synchronous with the proximal end, resulting in non-uniform rotational distortion (NURD) of the images. First, we apply a correction algorithm to remove NURD. We then use a speckle variance algorithm to identify vasculature. The initial data show a vascaulture density in small human airways similar to what would be expected.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".