Unsupervised Machine-learned Discovery of Quantitative Airway Tree Subtypes and Their Association With Chronic Obstructive Pulmonary Disease Endpoints: The Multi-Ethnic Study of Atherosclerosis Lung Study
Bibliographic record
Abstract
Abstract RATIONALE: Central airway tree topology is established in utero and varies in the general population. Visual airway tree assessment restricted to lower lobe segmental anatomy has identified absent and accessory airways in approximately 25% of adults and these variants are associated with higher chronic obstructive pulmonary disease (COPD) prevalence later in life, including in non-smoking study participants. This study applied unsupervised machine-learning to the full CT-resolved airway tree to discover novel quantitative airway tree subtypes (QATS) and evaluated the association of QATS with COPD endpoints. METHODS: The Multi-Ethnic Study of Atherosclerosis (MESA) Lung Study includes community-dwelling adults from six U.S. sites that underwent full-lung CT at suspended inspiration, spirometry, symptom questionnaires, genotyping and follow-up for respiratory hospitalization and/or death defined by primary diagnostic code. QATS were identified by clustering participant-level deep-learned representations of airway tree structures segmented from CT. We repeated clustering on five subsets of MESA Exam 5 (80% each) and quantified QATS reproducibility using the Rand Index. QATS differences in lung structure (mean airway lumen diameter, percent wall area, branch density, and geometric complexity), and COPD endpoints (forced expired volume in 1-sec / forced vital capacity [FEV1/FVC]<0.70, Medical Research Council [MRC] dyspnea rating >1, and rate of primary respiratory-related hospitalization or death) were assessed among all participants and among never smokers using regression models to adjust for age, sex, height, body mass index, race-ethnicity, total lung volume, cigarette smoking status and pack-years. RESULTS: Among 2,588 participants (mean±SD age: 69.4±9.3 years, 54% female, 51% ever smokers with 19±24 pack-years), four QATS clusters were identified (Table) and were 95% reproducible. Compared with QATS A (prevalence 30.3%; reference cluster), QATS B (prevalence 26.4%) was associated with higher prevalence of airflow obstruction, dyspnea and a higher rate of CLRD hospitalization or death among all participants and among never-smokers; QATS C (prevalence 22.6%) was associated with higher airflow obstruction prevalence but similar prevalence of dyspnea and CLRD events; and QATS D (prevalence 20.8%) was associated with higher airflow obstruction prevalence and percent of emphysema-like lung but similar dyspnea prevalence and rate of CLRD events. CONCLUSION: Unsupervised machine-learning identified four common quantitative airway tree subtypes (QATS) that differed with respect to COPD endpoints, including, as previously, participants who never smoked. These findings suggest that central airway tree topology varies considerably in the general population and is associated with distinct COPD phenotypes.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".