Automatic Airway Analysis on Multidetector Computed Tomography in Cystic Fibrosis
Bibliographic record
Abstract
PURPOSE: To evaluate the fully automatic quantification of airway dimensions on chest multidetector computed tomography (MDCT) performed in cystic fibrosis (CF) patients. Airflow indices including predicted forced expiratory volume in 1 second (FEV1%) were used to study the impact on regional lung function. MATERIALS AND METHODS: MDCT data of patients with CF (14 children and 23 adults) and of control patients (11 children and 22 adults) were used to compute total diameter (TD), lumen area (LA), and wall thickness (WT) using dedicated software. Pulmonary function testing including FEV1% was performed in parallel and correlated with MDCT parameters in a generation-based analysis. RESULTS: TD was largely increased in CF patients (third-generation to fourth-generation airways in children, first to ninth in adults; P<0.05). LA remained unchanged, but WT was also larger in CF compared with controls (third generation to sixth generation in children, first to eleventh in adults; P<0.05). In adult CF patients significant negative correlations for TD, LA, and WT with FEV1% were found for intermediate airways (fifth to seventh generation; r=-0.7 to -0.9) but not in pediatric CF patients and controls. CONCLUSIONS: Automatic airway analysis succeeded in quantifying specific pathologies such as airway dilatation and wall thickening in CF patients at different ages. Moreover, our results indicate a shift in main airflow resistance to intermediate airways in cases of chronic CF. The objective computational parameters TD, LA, and WT should be considered for assessment and follow-up of CF airway disease.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 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 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".