A Deep Learning–Based Automated Detection of Mucus Plugs in Chest CT

This study presents a novel two stage deep learning algorithm for automated detection of mucus plugs in CT scans of patients with respiratory diseases. Despite the clinical significance of mucus plugs in COPD and asthma where they indicate hypoxemia, reduced exercise tolerance, and poorer outcomes, they remain under evaluated in clinical practice due to labor intensive manual annotation. The developed algorithm first segments both patent and obstructed airways using a VNet-based model pre-trained on normal airway structures and fine-tuned on mucus containing scans. Subsequently, a rule-based post processing method identifies mucus plugs by evaluating cross sectional areas along airway centerlines. Validation on an in-house dataset of 33 CT scans from patients with asthma/COPD demonstrated high sensitivity (93.8%) though modest positive predictive value (18.8%). Performance on an external dataset (LIDC-IDRI) achieved 82.8% sensitivity with 23.5% PPV. While challenges remain in reducing false positives, this automated detection tool shows promise for screening applications in both clinical and research settings, potentially addressing the current gap in mucus plug evaluation within standard practice.