European radiology · 2026
Radiologist and AI performance in detecting mucus plugs on chest CT.
СтатьяНазвание и аннотация: Перевод в очереди
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Yoon Soonho, Kligerman Seth J, Friedlander Samuel R, Latišenko Rūdolfs, Charbonnier Jean-Paul, Tiddens Harm, Lynch David A, Humphries Stephen M
Аннотация
OBJECTIVES: To compare automated mucus plug detection with radiologist evaluation, assess AI-assisted detection, and examine associations of algorithm-derived airway obstruction burden with COPD severity.
MATERIALS AND METHODS: This retrospective analysis included 278 COPDGene participants selected across never-smokers, smokers with preserved ratio-impaired spirometry, and GOLD stages 0-4. An AI tool generated candidate plugs. Two thoracic radiologists independently identified plugs in unaided and AI-assisted sessions. A third thoracic radiologist adjudicated all candidates as the reference. Patient- and plug-level performance, observer agreement, false-positive detection, and rank-transformed associations with pulmonary function, CT parameters, and clinical severity were evaluated.
RESULTS: Reference mucus plugs were present in 114/278 participants (41%). Patient-level AI sensitivity was lower than R1 (68.4% vs 86.8%; p < 0.001) but not different from R2 (68.4% vs 79.8%; p = 0.141), whereas specificity was similar across approaches (95.7% vs 98.8%-100.0%). Plug-level AI sensitivity was lower than both readers (34.8% vs 58.9% for R1 and 55.6% for R2; both p < 0.001). AI assistance improved readers' plug-level sensitivity (63.6-68.6%; p ≤ 0.004). AI-reader agreement was good (κ, 0.62-0.69), and inter-reader agreement improved with AI (κ, 0.70 vs 0.78; p < 0.001). These non-reference-positive AI detections (0.396/scan) were mostly marked bronchial wall thickening or partially occlusive mucus. They were independently associated with worse airflow limitation, CT emphysema, Pi10, and clinical severity after accounting for true-positive burden, improving model fit (ΔR², 0.03-0.05).
CONCLUSION: AI was less sensitive than radiologists but had similar specificity for mucus plug detection. AI assistance improved reader sensitivity and agreement. Additional algorithm-detected airway abnormalities remained associated with airway disease severity.
KEY POINTS: QuestionAutomated mucus plug detection on chest CT may enable standardized COPD assessment, but independent validation against radiologist assessments remains limited. FindingsArtificial intelligence showed lower sensitivity than radiologists but similar patient-level specificity on chest CT, and assistance improved sensitivity and inter-reader agreement. Clinical relevanceAutomated mucus plug analysis may support standardized airway obstruction burden assessment in COPD research and clinical trials while retaining radiologist oversight.
MATERIALS AND METHODS: This retrospective analysis included 278 COPDGene participants selected across never-smokers, smokers with preserved ratio-impaired spirometry, and GOLD stages 0-4. An AI tool generated candidate plugs. Two thoracic radiologists independently identified plugs in unaided and AI-assisted sessions. A third thoracic radiologist adjudicated all candidates as the reference. Patient- and plug-level performance, observer agreement, false-positive detection, and rank-transformed associations with pulmonary function, CT parameters, and clinical severity were evaluated.
RESULTS: Reference mucus plugs were present in 114/278 participants (41%). Patient-level AI sensitivity was lower than R1 (68.4% vs 86.8%; p < 0.001) but not different from R2 (68.4% vs 79.8%; p = 0.141), whereas specificity was similar across approaches (95.7% vs 98.8%-100.0%). Plug-level AI sensitivity was lower than both readers (34.8% vs 58.9% for R1 and 55.6% for R2; both p < 0.001). AI assistance improved readers' plug-level sensitivity (63.6-68.6%; p ≤ 0.004). AI-reader agreement was good (κ, 0.62-0.69), and inter-reader agreement improved with AI (κ, 0.70 vs 0.78; p < 0.001). These non-reference-positive AI detections (0.396/scan) were mostly marked bronchial wall thickening or partially occlusive mucus. They were independently associated with worse airflow limitation, CT emphysema, Pi10, and clinical severity after accounting for true-positive burden, improving model fit (ΔR², 0.03-0.05).
CONCLUSION: AI was less sensitive than radiologists but had similar specificity for mucus plug detection. AI assistance improved reader sensitivity and agreement. Additional algorithm-detected airway abnormalities remained associated with airway disease severity.
KEY POINTS: QuestionAutomated mucus plug detection on chest CT may enable standardized COPD assessment, but independent validation against radiologist assessments remains limited. FindingsArtificial intelligence showed lower sensitivity than radiologists but similar patient-level specificity on chest CT, and assistance improved sensitivity and inter-reader agreement. Clinical relevanceAutomated mucus plug analysis may support standardized airway obstruction burden assessment in COPD research and clinical trials while retaining radiologist oversight.