Thirona’s LungQ® Becomes First AI Imaging Software CE-Certified for Automated Mucus Plug Analysis

The first and only certified automated mucus analysis, LungQ® MP marks a breakthrough in AI lung imaging, providing consistent quantitative insights into respiratory disease burden and treatment response. The certification is an important milestone for Thirona toward large-scale clinical implementation, with longitudinal plug tracking capabilities as the next step. 

NIJMEGEN, the Netherlands, 15 October 2026 – Thirona, a specialist in AI-powered lung image analysis, has obtained CE marking for fully automated mucus plug analysis (LungQ® MP) in LungQ® version 5, certified as a Class IIb medical device under the EU Medical Device Regulation (EU) 2017/745 (MDR). The software quantifies airway-occluding mucus plugs on chest CT within minutes, enabling assessment of how many mucus plugs are present, their volume and where they are located. This allows for patient phenotyping and monitoring of mucus burden changes in response to treatment.

The certification builds on clinical evaluation and a wider evidence base of more than 40 publications involving LungQ® MP. Key research span COPD^1,2, cystic fibrosis^3,4, bronchiectasis⁵ and asthma⁶, including studies demonstrating strong correlation with expert visual scoring^2,3 and specificity comparable to expert chest radiologists7, supporting its use in clinical trials and decision-making in clinical practice.

Standardized, objective mucus quantification in minutes 

LungQ® MP automatically maps mucus-occluded airways and reports plug count and plug volume per lung, lobe and bronchopulmonary segment, together with the mucus plug score (the number of segments containing at least one plug), with every analysis on the same scan producing identical results, free from inter-observer variability.

Together, these metrics characterize clinically significant mucus plug burden, with segment-level insights allowing clinicians to monitor which lung regions improve or worsen between time points.

Consistent structural insights at scale and over time

Quantitative mucus plug assessment is becoming an increasingly important imaging outcome measure in respiratory trials, complementing other clinical outcomes, such as lung function, exacerbations and inflammation, supporting treatment evaluation and characterization of mucus-related phenotypes.

Automated analysis applies the same criteria to every scan, delivering consistent results across large cohorts and multiple time points. This is essential in multicenter trials and treatment follow-up, where manual visual scoring is time-consuming and prone to reader variability.

 LungQ® MP has been evaluated and applied in clinical studies and cohorts across respiratory diseases, involving over 14,000 participants from COPDGene, the EMBARC registry, the ECFS registry, the MODUL-CF study and others. These studies demonstrate its ability to provide insights into the efficacy of therapies targeting mucus⁹˒¹⁰ and the heterogeneity of mucus burden across large patient cohorts⁸. They also show an association between high mucus plug burden and increased mortality risk in COPD, supporting its potential as a risk marker².

Building toward clinical implementation and individual mucus plug tracking

Responding to strong interest from pulmonologists, Thirona launched a training program for hospitals in Spain earlier this year in collaboration with SEPAR, the Spanish Society of Pulmonology and Thoracic Surgery, combining education on mucus assessment with the opportunity for pulmonologists to apply the analysis to their own patient cases.

With CE certification in place, Thirona is now rolling out LungQ® MP across Europe, starting with selected hospitals in Spain, the Netherlands and France, reaching more than 200 clinicians and bringing automated mucus analysis and a personalized patient report into everyday clinical practice. 

“This is an important step in turning our AI technology into reliable clinical tools, paving the way for wider adoption that will improve care for lung patients around the world – our guiding purpose,” says Dr. Eva van Rikxoort, CEO and founder of Thirona.

Next, building on today’s automated quantification, Thirona is developing AI software to track individual mucus plugs longitudinally across successive CT scans and assess whether treatment clears existing plugs, reduces new plug formation or leaves persistent obstruction. Because lung diseases often involve a varied mix of common structural lung changes, and each patient responds differently to therapy, such insights are key to personalizing treatment.

“Looking ahead, we will use these detailed insights to help pharmaceutical companies and clinicians ensure every patient receives the therapy that works for them,” adds van Rikxoort.

About Thirona

Thirona develops AI-powered software for lung image analysis. Its LungQ® portfolio combines anatomical segmentation with quantitative bronchial, vascular and parenchymal analysis, supporting pharmaceutical development, pulmonary interventions and clinical software integration.

Media contact: mediarelations@thirona.eu

More information: thirona.eu

Selected Evidence references.

1. Humphries et al. Concordance of visual and artificial intelligence–based mucus plug detection on computed tomography in the COPDGene study. American Journal of Respiratory and Critical Care Medicine. 2026;212(Suppl 1):aamag162.2063. https://doi.org/10.1093/ajrccm/aamag162.2063 
2. van der Veer T, et al. Association between automatic AI-based quantification of airway-occlusive mucus plugs and all-cause mortality in patients with COPD. Thorax. 2025;80:105–108. doi: 10.1136/thorax-2024-221928
3. Raut P, et al. Validation of an artificial intelligence-based automated PRAGMA and mucus plugging algorithm in pediatric cystic fibrosis. Journal of Cystic Fibrosis. 2025;24:970–978. doi:10.1016/j.jcf.2025.08.003.
4. Raut P, et al. Investigating imaging markers in cystic fibrosis preschoolers using manual and automated algorithms. European Respiratory Journal. 2025;66(Suppl 69):PA1611. https://doi.org/10.1183/13993003.congress-2025.PA1611 
5. van der Veer T, et al. Automated computed tomographic analysis of bronchial thickness and mucus plugs in bronchiectasis with asthma. ERJ Open Research. 2025;11(4):00736-2024. https://doi.org/10.1183/23120541.00736-2024
6. van der Veer T, et al. Airway thickening, mucus plugs and eosinophilia in bronchiectasis and asthma. European Respiratory Journal. 2024;64(Suppl 68):PA4194. https://doi.org/10.1183/13993003.congress-2024.PA4194
7. Yoon S, et al. Radiologist and AI performance in detecting mucus plugs on chest CT. European Radiology. 2026. doi:10.1007/s00330-026-12868-y.
8. Chen Y, et al. Radiological assessment of bronchial and arterial dimensions and mucus plug presence in 640 bronchiectasis patients: insights from the EMBARC registry. World Bronchiectasis Conference. 2025. Conference abstract, pp. 21–22.
9. McNally P, et al. Impact of 2 years of treatment with elexacaftor/tezacaftor/ivacaftor on longitudinal changes in structural lung disease in people with cystic fibrosis: results from the RECOVER trial. Annals of the American Thoracic Society. 2026;23:894–907. doi:10.1093/annalsats/aaoag021.
10. Krimsky WS, et al. Airway mucus plugging in chronic bronchitis and the impact of bronchial rheoplasty. CHEST. 2026;169:73–83. doi:10.1016/j.chest.2025.06.022.

For the full list of publications, visit: thirona.eu/publications