Preuves et publications
Efficacité / Gain de temps
  • 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).
Cancer du poumon


  • 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

(external publication) 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)

Parenchyme pulmonaire


  • 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


Predicting outcome with AI biomarkers

  • 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.