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., 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). https://doi.org/10.1186/s41747-020-00152-7
• Prayer, F., Röhrich, S., Pan, J. et al. Künstliche Intelligenz in der Bildgebung der Lunge. Radiologe 60, 42–47 (2020). https://doi.org/10.1007/s00117-019-00611-2
• 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)

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