Software development to optimize the minimal detectable difference in human airway images captured using optical coherence tomography
Bibliographic record
Abstract
Optical coherence tomography (OCT) is an imaging methodology that can be used to assess human airways. OCT avoids the harmful effects of ionizing radiation and has a high spatial resolution making it well suited for imaging the structure of small airways. Analysis of OCT airway images has typically been performed manually by tracing the airway with a relatively high coefficient of variation. The purpose of this study was to develop an analysis tool to reduce the inter- and intra-observer reproducibility of OCT and improve the ability to detect differences in airways. OCT images from healthy, young human volunteers were used to develop and test the OCT software. Measurement software was developed to allow the conversion of the original image into a grayscale image and was followed by an enhancement operation to brighten the image, and contour measurement. A total of 140 OCT images, 70 small (<2 mm) and 70 medium (2-4 mm) sized airways were analyzed. The inter- and intraobserver reproducibility of airway measurements ranged for strong to very strong in the small-sized airways. For medium-sized airways the reproducibility was considered moderate. Bland-Altman bias was low between observers and observations for all measures. The minimal detectable differences in the airway measurements with our semi-automated software were lower relative to manual tracing in medium-sized airways. Our software improves the ability to perform quantitative OCT analysis and may help to quantify the extent of airway remodelling in respiratory disease or elite athletes in future studies.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".