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 (B out ), bronchial inner wall (B in ) and artery (A), and bronchial wall thickness (B wt ) and computes BA ratios (B out /A and B in /A) to assess bronchial widening. To assess bronchial wall thickness, we used the B wt /A ratio and the ratio between the bronchus wall area (B wa ) and the area defined by the outer airway (B oa ) (B wa /B oa ). 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 B out /A, and 53 (42–65)% of bronchial wall were thickened using a cut-off value of 0.14 for B wt /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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 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) | 0.000 | 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 teacher head, 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".