Recherche
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) https://doi.org/10.1007/s00330-022-08973-3
    • 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

Malignancy 

  • Adams, S. et al. Clinical Impact and Generalizability of a Computer-Assisted Diagnostic Tool to Risk-Stratify Lung Nodules With CT. JACR (2022). https://doi.org/10.1016/j.jacr.2022.08.006
  • 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) https://doi.org/10.1016/j.jtho.2023.07.019

Calcium Scoring

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

Parenchyme pulmonaire

Emphysema

ILDs

  • 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)

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.

Fibrosis

Embolie pulmonaire accidentelle
  • 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).