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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".