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 (fSADTLC). Objectives To evaluate an AI model for estimating fSADTLC, compare it with dual-volume parametric response mapping fSAD (fSADPRM), 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 fSADTLC in the remaining 1,458 SPIROMICS participants. We compared fSADTLC with dual-volume fSADPRM. We investigated univariate and multivariable associations of fSADTLC 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 fSADTLC showed a strong correlation with fSADPRM in SPIROMICS (Pearson’s R = 0.895) and COPDGene (R = 0.897) cohorts. Higher fSADTLC 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 fSADTLC 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 fSADTLC. 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 fSADTLC. Inspiratory fSADTLC demonstrated greater consistency between repeated measurements, with a higher intraclass correlation coefficient of 0.99 (95% CI, 0.98–0.99) compared with fSADPRM (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 fSADPRM, demonstrates a significant association with FEV1 decline, and offers greater repeatability.