Airway morphometry in the lungs as depicted in chest CT examinations variability of measurements
Bibliographic record
Abstract
The purpose of the study was to decrease the variability of computed tomographic airway measurements. We to developed and evaluated a novel computer scheme to automatically segment airways depicted on chest CT examinations at the level of the lobar and segmental bronchi and to decrease. The computer scheme begins with manual selection of a seed point within the airway from which the airway wall and lumen are automatically segmented and airway pixels were assigned full or partial membership to the lumen or wall. Airway pixels not assigned full membership to the lumen (< -900 HU) or wall (> 0 HU) were assigned partial membership to the lumen and wall. In fifteen subjects with no visible signs of emphysema and a range of pulmonary obstruction from none to severe, airway measures were compared to pulmonary function parameters in a rank order analysis to evaluate measuring a single airway versus multiple airways. The quality of the automated airway segmentation was visually acceptable. The Pearson Correlation coefficients for the ranking of FEV1 versus wall area percent (percent of total airway size) and FVC versus wall area percent were 0.164 and 0.175 for a single measurement, respectively, and were 0.243 and 0.239 for multiple measurements, respectively. Our preliminary results suggest that averaging the measurements from multiple airways may improve the relation between airway measures and lung function compared to measurement from a single airway, which improve quantification of airway remodeling in COPD patients.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".