Abstract
Rationale: Quantifying functional small airway disease (fSAD) requires additional expiratory computed tomography (CT) scans, limiting clinical applicability. Artificial intelligence (AI) could enable fSAD quantification from chest CT scans at total lung capacity (TLC) alone (fSAD(TLC)). Objectives: To evaluate an AI model for estimating fSAD(TLC), compare it with dual-volume parametric response mapping fSAD (fSAD(PRM)), and assess its clinical associations and repeatability in chronic obstructive pulmonary disease (COPD). Methods: We analyzed 2,513 participants from SPIROMICS (the Subpopulations and Intermediate Outcome Measures in COPD Study). Using a randomly sampled subset (n = 1,055), we developed a generative model to produce virtual expiratory CT scans for estimating fSAD(TLC) in the remaining 1,458 SPIROMICS participants. We compared fSAD(TLC) with dual-volume fSAD(PRM). We investigated univariate and multivariable associations of fSAD(TLC) with FEV(1), FEV(1)/FVC ratio, 6-minute-walk distance, St. George's Respiratory Questionnaire score, and FEV(1) decline. The results were validated in a subset of patients from the COPDGene (Genetic Epidemiology of COPD) study (n = 458). Multivariable models were adjusted for age, race, sex, body mass index, baseline FEV(1), smoking pack-years, smoking status, and percent emphysema. Measurements and Main Results: Inspiratory fSAD(TLC) showed a strong correlation with fSAD(PRM) in SPIROMICS (Pearson's R = 0.895) and COPDGene (R = 0.897) cohorts. Higher fSAD(TLC) levels were significantly associated with lower lung function, including lower postbronchodilator FEV(1) (in liters) and FEV(1)/FVC ratio, and poorer quality of life reflected by higher total St. George's Respiratory Questionnaire scores independent of percent CT emphysema. In SPIROMICS, individuals with higher fSAD(TLC) experienced an annual decline in FEV(1) of 1.156 ml (relative decrease; 95% confidence interval [CI], 0.613-1.699; P < 0.001) per year for every 1% increase in fSAD(TLC). The rate of decline in the COPDGene cohort was slightly lower at 0.866 ml/yr (relative decrease; 95% CI, 0.345-1.386; P < 0.001) per 1% increase in fSAD(TLC). Inspiratory fSAD(TLC) demonstrated greater consistency between repeated measurements, with a higher intraclass correlation coefficient of 0.99 (95% CI, 0.98-0.99) compared with fSAD(PRM) (0.83; 95% CI, 0.76-0.88). Conclusions: Small airway disease can be reliably assessed from a single inspiratory CT scan using generative AI, eliminating the need for an additional expiratory CT scan. fSAD estimation from inspiratory CT correlates strongly with fSAD(PRM), demonstrates a significant association with FEV(1) decline, and offers greater repeatability.