Deep Learning Estimation of Small Airway Disease from Inspiratory Chest Computed Tomography: Clinical Validation, Repeatability, and Associations with Adverse Clinical Outcomes in Chronic Obstructive Pulmonary Disease

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 FEV₁, FEV₁/FVC ratio, 6-minute-walk distance, St. George's Respiratory Questionnaire score, and FEV₁ 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 FEV₁, 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 FEV₁ (in liters) and FEV₁/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 FEV₁ 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 FEV₁ decline, and offers greater repeatability.

Keywords: FEV1 decline; artificial intelligence; computed tomography; functional small airway disease.