• Woeltjen, M. et al. Impact of an online reference system on the diagnosis of rare or atypical abdominal tumors and lesions. Nature (2024). 
• Homelius M. and Stahlbrandt H. CT chest AI real-world evaluation in Jönköping county, Sweden.  ECR Abstract (2023).
• Röhrich, S. et al. Impact of a content-based image retrieval system on the interpretation of chest CTs of patients with diffuse parenchymal lung disease. European Radiology (2022). 
  • Results: 30% less time consumed when reading chest CTs with the software
• Agarwal, P. The next generation of reference books: Combining Content-Based Image Retrieval with a knowledge-based diagnostic decision support system in chest-CT. ECR Abstract (2022).

• Ayobi, A. et al. Performance Evaluation of an Artificial Intelligence (AI)-based Algorithm for Incidental Findings of Pulmonary Embolism. American Thoracic Society International Conference Abstract (2024).

Malignancy 

• Duerden, L. et al. A CADx tool improves lung nodule risk stratification when compared to British Thoracic Society guidelines on routine computed tomography (CT). Clinical Radiology (2026).
• Adams, S. et al. Clinical Impact and Generalizability of a Computer-Assisted Diagnostic Tool to Risk-Stratify Lung Nodules With CT. JACR (2022). 
• Calhoun, M. E. et al. Combining automated malignancy risk estimation with lung nodule detection may reduce physician effort and increase diagnostic accuracy. World Conference on Lung Cancer – IASLC Abstract (2022).

AI in lung cancer screening

• Sánchez-Cucóa, A. et al. Innovative Approach to Early Lung Cancer Detection: Integrating a Comprehensive Screening Program and an Incidental Pulmonary Nodule Clinic. Open Respiratory Archives (2025).
• S. Lam et al. Current and future perspectives on computed tomography screening for lung cancer: a roadmap from 2023 to 2027 from the international association for the study of lung cancer. J Thorac Oncol (2023). https://doi.org/10.1016/j.jtho.2023.07.019

Incidental Detection

• Dunsche, J. et al. DETECT IPN: Real-World Experience with Automated Detection of Incidental Pulmonary Nodules in an All-Comer Population. Scientific Research. (2025).

Calcium Scoring

• G. Deodato, et al. Effects of scan reconstruction on cardiovascular disease risk assessment using Agatston scoring. (European Congress of Radiology 2024).

Emphysema

• Belde, D. et al. Comparative evaluation of emphysema quantification: Standardized %LAV-950 versus DL-based emphysema quantification with clinical parameter correlation. Medicine (2025).
• Hofmanninger, J. et al. Deep learning-based quantification of emphysema on low-dose CT for lung cancer risk assessment. ECR Abstract (2025).
• Perkonigg, M. et al. Comparing Emphysema Detection based on a Threshold and Deep Learning. ECR Abstract (2023).
• contextflow emphysema detection model can contribute to better emphysema detection with less false positives: a contextflow whitepaper. https://contextflow.com/2022/10/27/why-hu-may-not-be-the-best-approach-to-emphysema-quantification-a-contextflow-whitepaper/

ILDs

• Baron, M. et al. Non-contiguous Computed Tomography Lung Scans Can be Manipulated to Permit Artificial Intelligence Analyses for Interstitial Lung Disease in Systemic Sclerosis. medRxiv (2025).
• Juskanich, D. et al. Establishing normal lung volume thresholds through AI for CT analysis. ECR Abstract (2025).
• Janska E. et al. Tracking Disease Progression in Fibrotic Interstitial Lung Disease with Quantitative CT. ECR Abstract (2025).
• Improving lung segmentation for higher coverage of clinically-relevant findings: a contextflow whitepaper. (September 2023). https://contextflow.com/wp-content/uploads/2023/09/lung-segmentation-whitepaper_final.pdf 
• Röhrich, S. et al. Evaluation of diagnosing diffuse parenchymal lung disease in pulmonary CTs (2022). European Society of Thoracic Imaging/ESTI Abstract (2022).
• Pieler, M. et al. Evaluation of automatic volumetry of honeycombing and ground glass opacity patterns in lung CT scans. ECR Abstract (2022). https://dx.doi.org/10.26044/ecr2022/C-15193 (EPOS™ – C-15193) (myesr.org)) 
• Röhrich, S. Impact of a content-based image retrieval system on the interpretation of chest CTs of patients with diffuse parenchymal lung disease. ECR Abstract (2022). 
• Prayer, F., Röhrich, S., Pan, J. et al. Künstliche Intelligenz in der Bildgebung der Lunge. Radiologe 60, 42–47 (2020).
• Röhrich, S., Schlegl, T., Bardach, C. et al. Deep learning detection and quantification of pneumothorax in heterogeneous routine chest computed tomography. European Radiology Exp 4, 26 (2020). 

COVID-19

• Visual and AI-Based Assessment of COVID-19 Pneumonia: Practicability and Reproducibility of an Established Semi-Quantitative Chest CT Scoring System. MDPI (2025).

Predicting outcome with AI biomarkers

• Straub, J. et al. Artificial intelligence in respiratory pandemics—ready for disease X? A scoping review. European Radiology (2024).
• Pan, J. et al. Prediction of disease severity in COVID-19 patients identifies predictive disease patterns in lung CT (2022). European Society of Thoracic Imaging/ESTI 2022 (June, Oxford).
• Halfmann, M. et al. Pre-interventional AI-supported automated lung parenchyma quantification predicts post-interventional complications in CT-guided lung biopsies. ECR Abstract (2023).
  • AI-based lung texture analysis has potentially a predictive value for complications. Greater amount of pre-interventional consolidations (p=0.03) and smaller lesion size (p=0.04) were predictors for post-interventional pneumothorax.

Fibrosis

• Pan, J. et al. Deep learning quantifies disease patterns in lung CT associated with individual outcome in idiopathic pulmonary fibrosis (2023).
  • Patterns honeycombing and reticulation have a predictive power for individual patient outcome (death) in IPF patients.
  • Using contextflow you can predict future severity/outcome (survival) in lung fibrosis
• K. Akbari et al. Prognostic implications of clinical imaging and blood biomarkers on progression in fibrotic interstitial lung diseases using quantitative CT analysis (2023).
  • Looking for a correlation between of and lung volumetrics with lung function in fibrotic patients

Leiden University Medical Center (NL)
Region Jönköpings Iän (SE)
University Medical Center – Johannes Gutenberg-University Mainz (DE)
Cochin Hospital (FR)

Cambridge University (UK)
Medical University Vienna (AT)
Charité Berlin (DE)
Kepler University Hospital Linz (AT)
Medical University Düsseldorf (DE)
Innsbruck University Hospital (AT)
Jessenius – Diagnostic Centre a.s. (SK)
Spanish Society of Rheumatology/La Paz University (ES)
International Atomic Energy Agency (IAEA)

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Erasmus Medical Center, Department of Radiology in Rotterdam
Erasmus School of Health Policy and Management (ESHPM), Department of Health Technology Assessment in Rotterdam
Technical University Delft, Department of Imaging Physics in Delft