Augmented diagnosis, quantification, characterization, image reconstruction: medical artificial intelligence at your fingertips for radiology
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Transpara

AI solution for breast cancer detection and diagnosis

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Transpara assists radiologists in their interpretation of a mammography exam. Transpara provides a probability of the presence of breast cancer based on automatically detected and AI-scored lesions.

Automatic lesion detection: soft tissue lesions and microcalcifications

Segmentation and contouring of suspicious regions.

Evaluation of the level of malignancy of each lesion detected.

Definition of a regional score for each microcalcification and suspicious tissue lesion.

Analysis of the overall probability that cancer is present in a mammogram.

Categorization of the examination based on an overall score.

  • breast cancer
  • microcalcifications
  • tomosynthesis
  • mammography
  • soft tissue injury
  • 3D
  • lesion

Increase confidence in your diagnosis

Increase confidence in your diagnosis, both for normal and suspected cases, thanks to the dual reading of the AI

Adds another layer of security

Adds another layer of security with alerts that provide global test scores

Identify suspicious lesions faster

Identify and qualify benign or malign lesions faster in suspecious regions

Save time, reassure your diagnosis and streamline your workflow with Incepto

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Testimonial

When I see a score of 1 or 2, even in dense breasts, I become very calm. Obviously, I analyze the mammogram very carefully, but more calmly and quickly than before, which leaves me more time to focus on the less favorable results. On the other hand, when the scores are 8, 9 and 10, I trust the abnormalities indicated by Transpara and I am even more attentive to my analysis.

Dr Marc Abehsera,

Senologist and radiologist
Paris American Hospital

Publications

  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:https://doi.org/10.1148/radiol.2018182404.
  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).

Save time, secure diagnosis and optimize your workflow with Incepto

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