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Evidence


ScreenPoint has assembled a world-class team of researchers and engineers with extensive experience in breast imaging, machine learning, algorithm development, and clinical research. The team is highly motivated to develop technology that can make a difference in the early detection of breast cancer. Close collaboration with radiologists ensures that products meet clinical needs and fit in the workflow of a busy screening practice. Research is aimed at achieving superior performance of detection algorithms and on understanding the impact of our device Transpara™ in clinical practice.

Transpara in Clinical Practice

A. Rodriguez-Ruiz, K. Lång, A. Gubern-Merida, M. Broeders, G. Gennaro, P. Clauser, T. Helbich, m. Chevalier, T. Tan, T. Mertelmeier, W. Wallis, I. Andersson, S. Zackrisson, R. Mann, I. Sechopoulos. Stand-alone artificial intelligence for breast cancer detection in mammography: Comparison with 101 radiologists. Journal of the National Cancer Institute 2019;111(9):djy222. https://doi.org/10.1093/jnci/djy222

A. Rodriguez-Ruiz, E. Krupinski, J. Mordang, K. Schilling, S. Heywang-Kobrunner, I. Sechopoulos, R. Mann. Detection of breast cancer using mammography: Impact of an Artificial Intelligence support system. Radiology 2019;290 (2), 305-314. https://doi.org/10.1148/radiol.2018181371

A. Rodriguez-Ruiz, K. Lång, A. Gubern-Merida, J. Teuwen, M. Broeders, G. Gennaro, P. Clauser, T. Helbich, M. Chevalier, T. Tan, T. Mertelmeier, W. Wallis, I. Andersson, S. Zackrisson, I. Sechopoulos, R. Mann. Can we reduce the workload of mammographic screening by automatic identification of normal exams with artificial intelligence? A feasibility study. European Radiology 2019;29:4825. https://doi.org/10.1007/s00330-019-06186-9

Lång, K., Dustler, M., Dahlblom, V. et al. Identifying normal mammograms in a large screening population using artificial intelligence. Eur Radiol (2020). https://doi.org/10.1007/s00330-020-07165-1

R. Hupse, M. Samulski, M.B. Lobbes, R.M. Mann, R. Mus, G.J. den Heeten, D. Beijerinck, R.M. Pijnappel, C. Boetes and N. Karssemeijer. Computer-aided Detection of Masses at Mammography: Interactive Decision Support versus Prompts. Radiology 2013;266:123-129.

R. Hupse, M. Samulski, M. Lobbes, A. den Heeten, M.W. Imhof-Tas, D. Beijerinck, R. Pijnappel, C. Boetes and N. Karssemeijer. Standalone computer-aided detection compared to radiologists' performance for the detection of mammographic masses. European Radiology 2013;23:93-100.

M. Samulski, R. Hupse, C. Boetes, R. Mus, G. den Heeten and N. Karssemeijer. Using Computer Aided Detection in Mammography as a Decision Support. European Radiology 2010;20:2323-2330.

M Sasaki, M Tozaki, A Rodríguez-Ruiz, D Yotsumoto, Y Ichiki, A Terawaki, S Oosako, 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.  ISSN 1340-6868, DOI 10.1007/s12282-020-01061-8

 

From ECR 2020

Dahlblom.  CTiR 3 - Can additional cancers detected by digital breast tomosynthesis in screening be detected on the corresponding mammography examination using artificial intelligence?

K. Lang. RPS 605b - Can artificial intelligence reduce the interval cancer rate in mammography screening?

A. Lauritzen. RPS 702 - Reducing the radiologist's workload by detecting normal mammograms with an AI system

 

From IWBI 2020

Balta, A. Rodriguez-Ruiz, C. Mieskes, N. Karssemeijer, S. H. Heywang-Köbrunner, Going from double to single reading for screening exams labeled as likely normal by AI: what is the impact?, Proc. SPIE 11513, 15th International Workshop on Breast Imaging (IWBI2020), 115130D (22 May 2020); doi: 10.1117/12.2564179

Dustler, V. Dahlblom, A. Tingberg, S. Zackrisson, The effect of breast density on the performance of deep learning-based breast cancer detection methods for mammography, Proc. SPIE 11513, 15th International Workshop on Breast Imaging (IWBI2020), 1151324 (22 May 2020); doi: 10.1117/12.2564328

Dahlblom, A. Tingberg, S. Zackrisson, M. Dustler, Personalised breast cancer screening with selective addition of digital breast tomosynthesis through artificial intelligence, Proc. SPIE 11513, 15th International Workshop on Breast Imaging (IWBI2020), 115130C (22 May 2020); doi: 10.1117/12.2564344

Rodríguez-Ruiz, K. Lång, A. Gubern-Merida, M. Broeders, G. Gennaro, P. Clauser, T. Helbich, T. Mertelmeier, M. Chevalier, M. Wallis, I. Andersson, S. Zackrisson, R. M. Mann, I. Sechopoulos, Can AI serve as an independent second reader of mammograms? a simulation study, Proc. SPIE 11513, 15th International Workshop on Breast Imaging (IWBI2020), 115130O (22 May 2020); doi: 10.1117/12.2564114

Bejnö, G. Hellgren, A. Rodriguez-Ruiz, P. R. Bakic, S. Zackrisson, A. Tingberg, M. Dustler, Artificial intelligence together with mechanical imaging in mammography” Proc. SPIE 11513, 15th International Workshop on Breast Imaging (IWBI2020), 1151320 (22 May 2020); doi: 10.1117/12.2564107

ScreenPoint Medical Participates in the Following Funded Research Projects:

 

InMediValue

Funded by The European Regional Development Fund Netherlands/Germany, aims to assess and optimize breast cancer detection techniques in mammograms.

MARBLE

Funded by The European Regional Development Fund East Netherlands, aims to develop a clinically validated CAD system for mammography that is able to make optimal use of prior mammograms.

IBSCREEN

Funded by the Eurostars countries and by the European Union, aims to enhance detection algorithms in mammography and breast tomosynthesis by using the latest deep learning techniques.