Breast Cancer

Transpara by ScreenPoint

The software solution that will change the way you read mammograms

A Challenging Task

With more than 50,000 breast cancers diagnosed in France and 4.4 million mammography exams performed every year, Breast cancer screening is challenging, even to the expert eye. Indeed, not missing cancers, while not calling too many False Positives, is a delicate mission.

What if an AI solution could automatically categorize mammograms more likely to have cancerous lesions?

Trained on over 1 million images, utilizing revolutionary deep learning technology, Transpara automatically identifies suspicious areas in 2D and 3D mammograms.
Interactive decision support, a scientifically-proven method (3), boosts Radiologists reading performance, as opposed to traditional mammography CAD systems.

Based on all findings of the exam, each case is categorized using the innovative Transpara Score, based on its risk of harbouring cancer and can be used to automatically triage exams with confidence.

Expected benefits

  • Optimizing your workflow by allocating your attention to the most complex cases,
  • Increasing your confidence on the low risk cases,
  • Reducing Tomography reading time,
  • Reducing interpretation errors and improving reading performance,
  • Integrating a decision support tool into your workflow – without slowing you down.

30 years of Expertise

Pr Nico Karssemeijer and Sir Mike Brady, two world leading experts in quantitative breast image analysis and computer aided detection, are the co-founders of ScreenPoint Medical.

Nico has been one of the pioneers of computer-aided detection, playing a key role in implementing digital mammography in the European screening programs, by initiating and leading EU funded projects in which breast screening workstations were developed that are marketed today by major vendors. He is a Professor in the Department of Radiology, Radboud University, Nijmegen, The Netherlands.


  1. Schaffter, T. et al. Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms. JAMA Netw Open 3, e200265 (2020).
  2. Sasaki, M. et al. Artificial intelligence for breast cancer detection in mammography: experience of use of the ScreenPoint Medical Transpara system in 310 Japanese women. Breast Cancer 27, 642–651 (2020).
  3. Lång, K. et al. Identifying normal mammograms in a large screening population using artificial intelligence. Eur Radiol (2020) doi:10.1007/s00330-020-07165-1.
  4. Rodriguez-Ruiz, A. et al. Can we reduce the workload of mammographic screening by automatic identification of normal exams with artificial intelligence? A feasibility study. Eur Radiol 29, 4825–4832 (2019).
  5. Rodriguez-Ruiz, A. et al. Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists. 7 (2019).
  6. Le, E. P. V., Wang, Y., Huang, Y., Hickman, S. & Gilbert, F. J. Artificial intelligence in breast imaging. Clinical Radiology 74, 357–366 (2019).
  7. Bahl, M. Detecting Breast Cancers with Mammography: Radiology 2 (2019) doi:
  8. Rodríguez-Ruiz, A. et al. Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System. Radiology 181371 (2018) doi:10.1148/radiol.2018181371.
  9. Hupse, R. et al. Computer-aided Detection of Masses at Mammography: Interactive Decision Support versus Prompts. Radiology 266, 123–129 (2013).