Deep Learning Estimation of Small Airways Disease from Inspiratory Chest CT: Clinical Validation, Repeatability, and Associations with Adverse Clinical Outcomes in COPD

Quantifying functional small airways disease (fSAD) requires additional expiratory computed tomography (CT) scan, limiting clinical applicability. Artificial intelligence (AI) could enable fSAD quantification from chest CT scan at total lung capacity (TLC) alone (fSADTLC). 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). We analyzed 2513 participants from the SubPopulations and InteRmediate Outcome Measures in COPD Study (SPIROMICS). Using a randomly sampled subset (n = 1055), we developed a generative model to produce virtual expiratory CTs for estimating fSADTLC in the remaining 1458 SPIROMICS participants. We compared fSADTLC with dual volume, parametric response mapping fSADPRM. We investigated univariate and multivariable associations of fSADTLC with FEV1, FEV1/FVC, six-minute walk distance (6MWD), St. George's Respiratory Questionnaire (SGRQ), and FEV1 decline. The results were validated in a subset (n = 458) from COPDGene study. Multivariable models were adjusted for age, race, sex, BMI, baseline FEV1, smoking pack years, smoking status, and percent emphysema. Inspiratory fSADTLC showed a strong correlation with fSADPRM in both 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 (L) and FEV1/FVC ratio, and poorer quality of life reflected by higher total SGRQ 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% CI: 0.613, 1.699; P < 0.001) per year for every 1% increase in fSADTLC. The rate of decline in COPDGene was slightly lower at 0.866 mL / year (relative decrease; 95% CI: 0.345, 1.386; P < 0.001) for percent increase in fSADTLC. Inspiratory fSADTLC demonstrated greater consistency between repeated measurements with a higher intraclass correlation coefficient (ICC) of 0.99 (95% CI: 0.98, 0.99) compared to fSADPRM [ICC: 0.83 (95% CI: 0.76, 0.88)]. Small airways 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.