Artificial Intelligence-Based Detection of Airway Mucus Plugs on CT and Associations With Clinical Outcomes in COPDGene
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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 FEV₁ % predicted (ρ = −0.41; P < 0.001), greater air trapping (LAA < −856 HU; ρ = 0.33; P < 0.001), worse health status (SGRQ; ρ = 0.31; P < 0.001), and shorter 6-minute walk distance (ρ = −0.26; P < 0.001). Among GOLD 1–4 participants, mucus plug presence was independently associated with increased all-cause mortality (adjusted hazard ratio, 1.28; P < 0.005) and exacerbation frequency (adjusted incidence rate ratio, 1.32; P < 0.005). Plug presence was also associated with increased respiratory mortality across GOLD categories and cardiovascular mortality in GOLD 1–2.
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.
