Automated method of bronchus and artery dimension measurement in an adult bronchiectasis population
Bibliographic record
Abstract
Aim Bronchiectasis (BE) is a disease defined by irreversible dilatation of the airway. Computed tomography (CT) plays an important role in the detection and quantification of BE. The aim of this study was three-fold: 1) to assess bronchus–artery (BA) dimensions using fully automated software in a cohort of BE disease patients; 2) to compare BA dimensions with semi-quantitative BEST-CT (Bronchiectasis Scoring Technique for CT) scores for BE and bronchial wall thickening; and 3) to explore the structure–function relationship between BA-method lumen dimensions and spirometry outcomes. Methods Baseline CTs of BE patients who participated in a clinical trial were collected retrospectively. CTs were analysed manually with the BEST-CT scoring system and automatically using LungQ (v.2.1.0.1, Thirona, The Netherlands), which measures the following BA dimensions: diameters of bronchial outer wall (Bout), bronchial inner wall (Bin) and artery (A), and bronchial wall thickness (Bwt) and computes BA ratios (Bout/A and Bin/A) to assess bronchial widening. To assess bronchial wall thickness, we used the Bwt/A ratio and the ratio between the bronchus wall area (Bwa) and the area defined by the outer airway (Boa) (Bwa/Boa). Results In total, 65 patients and 16 900 BA pairs were analysed by the automated BA method. The median (range) percentage of BA pairs defined as widened was 69 (55–84)% per CT using a cut-off value of 1.5 for Bout/A, and 53 (42–65)% of bronchial wall were thickened using a cut-off value of 0.14 for Bwt/A. BA dimensions were correlated with comparable outcomes for the BEST-CT scoring method with a correlation coefficient varying between 0.21 to 0.51. The major CT BA determinants of airflow obstruction were bronchial wall thickness (p=0.001) and a narrower bronchial inner diameter (p=0.003). Conclusion The automated BA method, which is an accurate and sensitive tool, demonstrates a stronger correlation between visual and automated assessment and lung function when using a higher cut-off value to define bronchiectasis.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".