Artificial Intelligence-Based Detection of Airway Mucus Plugs on CT and Associations With Clinical Outcomes in COPDGene.

RATIONALE –: Airway mucus plugging is a clinically relevant manifestation of airway pathology in chronic obstructive pulmonary disease (COPD) and is associated with increased mortality even in early disease; however, visual computed tomography (CT) assessment is subjective and labor intensive. OBJECTIVES –: To develop an AI-based quantitative CT method for automated detection of airway mucus plugging and evaluate associations with physiologic impairment and clinical outcomes. METHODS –: Inspiratory CT scans from 8,971 COPDGene Phase 1 (GOLD 0-4 and PRISm) participants were analyzed. An AI-based framework combining 3D airway segmentation discontinuities and convolutional neural network classification identified mucus plug obstructions, yielding mucus plug burden (total plug count). Associations with outcomes were evaluated using covariate-adjusted models. MEASUREMENTS AND MAIN RESULTS –: Higher mucus plug burden was associated with lower post-bronchodilator FEV CONCLUSIONS –: AI-based quantitative CT assessment of airway mucus plugging provides a scalable, reproducible measure associated with physiologic impairment and adverse outcomes in COPD, supporting its role in risk stratification and future therapeutic studies.