Fully automated quantification of mucus obstruction, in minutes
Advancing treatment of muco-obstructive airway diseases
As a key feature of chronic respiratory disease, mucus plugging is increasingly recognized as a clinically relevant imaging metric and therapeutic target, with growing importance in both clinical research and the development of novel therapies. Thirona's AI-derived LungQ® Mucus Plug analysis (LungQ® MP) has been extensively validated across global research initiatives including COPDGene, EMBARC, and ENRICH and utilized to evaluate effectiveness of new therapies.
Furthermore, these quantitative metrics continue to be explored as potential imaging biomarkers for disease management in clinical practice. By providing precise quantification of mucus plug burden, LungQ® MP delivers the objective data insights that enable:
- Reproducible longitudinal assessment for disease progression and treatment response evaluation
- Regional insights into airway obstruction, supporting deeper understanding of mucus disease mechanisms
- Characterization of mucus-dominant phenotypes, capturing patient heterogeneity across bronchial tree
- AI-derived imaging insights enhancing prediction of disease trajectory and patient risk assessment
High-precision sensitive imaging metrics
Leveraging advanced algorithms to anatomically segment the bronchial tree, LungQ® MP identifies and quantifies airway-occluding mucus plugs on CT. The analysis provides precise measurements of mucus plug count, volume, segment score, and density, quantified on lung, lobar and segmental levels.
Total number
of plugs per lung, lobe and segment
Total volume
of plugs per lung, lobe and segment
Segment score
number of segments with mucus plugs
Plug density
in Hounsfield Unit values per lung, lobe and segment
Notes: Availability and intended use of the analysis may vary by geography; LungQ® Mucus plug count, volume and segment score are pending CE marking in the European Union and are not approved for clinical use in the United States; LungQ® Mucus density is currently intended for clinical research use and is not approved for clinical use (in the United States and European Union)
Clinical application of AI-driven imaging insights
Exploring the role of mucus plugs in disease progression through longitudinal cohort analysis
The 10-year COPDGene study, utilizing LungQ automated mucus plug measurements and bronchial measurements demonstrates a strong link between disease progression and key structural airway changes from baseline to 10-year follow-up. Notably, disease progression is significantly associated with a higher occurrence of mucus plugs, which is clearly observable in the progressor group.
Validation studies
American Thoracic Society Conference
| May 2025
Assessment of 10-year Progression in COPDGene Using Automated Measurements of Bronchus-Artery Ratios and Mucus Plugging
European Respiratory Journal
| November 2025
COPD progressors vs. non-progressors: 10-year change in Bronchus-Artery ratios & mucus plugs
Primary/secondary outcome measures for clinical trial
LungQ®MP demonstrates performance comparable to expert visual assessment methods for mucus plug detection and quantification. This analysis enables a detailed evaluation of mucus plug severity and regional distribution, offering high-resolution insights into lobar involvement. By capturing the heterogeneity of mucus plug burden both across large patient cohorts and within the individual patient, it provides the objective data necessary to serve as a reliable primary or secondary outcome measure
Validation studies
American Journal of Respiratory and Critical Care Medicine
| June 2026
Concordance of Visual and Artificial Intelligence-Based Mucus Plug Detection on Computed Tomography in the COPDgene Study
European Respiratory Journal
| January 2026
Automatic detection of mucus plugs on computed tomography scans in severe paediatric asthma
European Respiratory Journal (ERS Congress)
| August 2024
Mucus Plug Lobar Distribution in COPD Using Automatic AI-Based Mucus Plug Quantification
Precise phenotyping based on mucus burden
Precise phenotyping is essential for the success of respiratory clinical trials. Capturing the severity of mucus plug burden both across large patient cohorts and across individual lobes, LungQ®MP provides the quantitative metrics enabling more accurate patient selection and the enrichment of clinical trial populations.
By integrating with other bronchial metrics, it provides a comprehensive view of lung structural abnormalities to support integrated structural phenotyping, which is critical for identifying distinct clinical phenotype.
Validation studies
European Respiratory Journal
| January 2026
Automatic detection of mucus plugs on computed tomography scans in severe paediatric asthma
European Respiratory Journal (ERS Congress)
| October 2024
Automatic analysis of bronchus-artery ratios and mucus plugs of 640 chest CTs of EMBARC bronchiectasis patients
Journal of Cystic Fibrosis
| August 2025
Validation of an artificial intelligence-based automated PRAGMA and mucus plugging algorithm in pediatric cystic fibrosis
Response evaluation of pharmaceutical and interventional treatment targeting mucus reduction
With the longitudinal insights into treatment response and the sensitive detection of subtle anatomical changes, LungQ®MP supports the evaluation of treatment efficacy across the full patient journey from early treatment response to long-term durability of therapeutic effects.
The analysis provides sensitive metrics at both global and regional levels to evaluate the efficacy of therapies targeting key mucus reduction mechanisms, including improvement of mucus rheology and clearance, restoration of airway surface hydration, and mitigation of chronic or eosinophilic inflammatory responses, as well as interventional approaches addressing mucosal inflammation and hypersecretion (e.g. bronchial rheoplasty).
Validation studies
The Lancet Respiratory Medicine
| October 2025
Effect of elexacaftor–tezacaftor–ivacaftor on bronchial dilatations in adolescents with cystic fibrosis: a multicentre prospective observational study
Chest
| July 2025
Airway mucus plugging in chronic bronchitis and the impact of Bronchial Rheoplasty
European Respiratory Journal
| January 2026
The effect of Elexacaftor/Tezacaftor/Ivacaftor (ETI) on bronchial tapering as marker of bronchial dilatation in people with CF aged 12 above (RECOVER study)
American Journal of Respiratory and Critical Care Medicine
| May 2024
Bronchial Rheoplasty Reduces Mucus Plugging in Patients With Chronic Bronchitis
European Respiratory Journal (ERS Congress)
| October 2024
Automatic analysis of bronchus-artery ratios and mucus plugs of 640 chest CTs of EMBARC bronchiectasis patients
Research Square
| October 2025
The impact of dornase alfa on imaging features of bronchiectasis
Enhancing prediction of disease trajectories and patient risk assessment
Exploring clinical associations, characterizing disease trajectories, and enhancing risk prediction are essential for effective patient monitoring, treatment selection, and long-term prognosis management. LungQ® MP facilitates the identification of patients with high mucus burden, supporting personalized disease management and precise risk stratification. Furthermore, this analysis enables the longitudinal monitoring of disease progression, allowing clinicians to correlate the imaging metrics with clinical outcomes to predict disease trajectories including future exacerbations and mortality risk.
