Segmental airway phantom for endobronchial optical coherence tomography
Bibliographic record
Abstract
MOTIVATION: Biological samples are not always available to validate performance during development of optical imaging devices for in vivo detection of potentially malignant lesions. Thus, to provide readily available testing, there is a need for phantoms with optical response similar to that of target tissue. OBJECTIVES: 1) Fabricate lung tissue phantoms that mimic structural and optical properties of central and segmental airways for 1310 ± 50 nm endoscopic OCT. 2) Simulate vascular flow to characterize angiography. 3) Produce a robust and cost-effective alternative to ex vivo tissue. METHODS: An agar matrix is mixed with intralipid and coconut oil to achieve tissue-like absorption and scattering properties. A partitioned 3D printed mould is used to mimic airway geometry and embedded tubing is used to simulate vasculature. Fluid-flow is visualized with inter-A-line speckle decorrelation methods. Phantom optical performance is qualitatively and quantitatively compared against segmental airways in previous in vivo human studies using the same imaging system. RESULTS: Images of common bronchial structures (eg: ducts, airway branches) reproduced in the phantoms qualitatively resemble similar structures in vivo (lung airway LB9) in OCT. Airway epithelial thickening indicative of dysplastic progression in vivo is re-created in the phantoms. Depth resolved attenuation coefficients are calculated and plotted for images collected on the same system, quantitatively characterizing replication. Live vasculature is mimicked using intralipid flow and visualized.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".