• Improving lung segmentation for higher coverage of clinically-relevant findings (September 2023).
  • Here we describe and evaluate a lung segmentation algorithm that yields high accuracy despite disease patterns thanks to a 3D architecture and diverse training data set. We compare our algorithm with the established state-of-the-art algorithm.
• Why HU may not be the best approach to emphysema quantification. (October 2022). Emphysema quantification with contextflow SEARCH Lung CT
  • AI is able to measure the extent of emphysema more accurately, shows more accurate detection results when quantified in AI than HU.

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., 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

Röhrich, S., Heidinger, B.H., Prayer, F. 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). https://doi.org/10.1007/s00330-022-08973-3

This study shows 30% less time consumed when reading chest CTs with AI
Adams, S., Madtes, K., Burbridge, B. et al. Clinical Impact and Generalizability of a Computer-Assisted Diagnostic Tool to Risk-Stratify Lung Nodules With CT. JACR, in press (2022).
https://doi.org/10.1016/j.jacr.2022.08.006

contextflow in clinical use showcased at ECR 2023 in Vienna

ECR 2023 (March, Vienna)

• Halfmann, M. et al. Pre-interventional AI-supported automated lung parenchyma quantification predicts post-interventional complications in CT-guided lung biopsies (2023)
• Pan, J. et al. Deep learning quantifies disease patterns in lung CT associated with individual outcome in idiopathic pulmonary fibrosis (2023)
• Perkonigg, M. et al. Comparing Emphysema Detection based on a Threshold and Deep Learning (2023)

European Society of Thoracic Imaging/ESTI 2022 (June, Oxford)

• Röhrich, S. et al. Evaluation of diagnosing diffuse parenchymal lung disease in pulmonary CTs (2022).
• Pan, J. et al. Prediction of disease severity in COVID-19 patients identifies predictive disease patterns in lung CT (2022).

ECR 2022 (July, Vienna)

• Pieler, M. et al. Evaluation of automatic volumetry of honeycombing and ground glass opacity patterns in lung CT scans (2022).
• Röhrich, S. Impact of a content-based image retrieval system on the interpretation of chest CTs of patients with diffuse parenchymal lung disease (2022).
• Agarwal, P. The next generation of reference books: Combining Content-Based Image Retrieval with a knowledge-based diagnostic decision support system in chest-CT (2022).

World Conference on Lung Cancer – IASLC 2022 (August, Vienna)

• Calhoun, M. E. et al. Combining automated malignancy risk estimation with lung nodule detection may reduce physician effort and increase diagnostic accuracy (2022).

NL/Leiden University Hospital (LUMC)
SE/Jönköping Hospitals
DE/Medical University Mainz

Cambridge University (UK)
Medical University Vienna (AT)
Charité Berlin (DE)
Kepler University Hospital Linz (AT)
Medical University Düsseldorf (DE)
Innsbruck University Hospital (AT)

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