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Transpara

Transpara is a software device for automated detection of regions in mammograms suspicious for breast cancer.1,2

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Transpara
  • Breast Cancer
  • Reading Aid
  • X-Ray

The Global Burden of Breast Cancer

Nearly 700,000 deaths from breast cancer occurred in 2020 and the global breast cancer burden is predicted to increase to over 3 million new cases and 1 million deaths every year by 2040.3

 

 

A Concurrent Reading Aid for Radiologists

Transpara software is intended for use as a concurrent reading aid for radiologists during interpretation of 2D and 3D mammograms of asymptomatic women to identify regions suspicious for breast cancer. Output of the device contains region-based scores indicating the likelihood that cancer is present in specific regions and an overall score indicating the likelihood that cancer is present on the mammogram.1

 

Speed. Performance.
Efficiency.

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Improves radiologists' reading time per 3D exam1,2-6

Screen Image

Potentially improves screening outcomes7-8

Timer Icon

May reduce workload7-8

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Applications Tailored to You

Discover how our tailored service lines and applications can better suit your needs.

Find Out More

    1. ScreenPoint Transpara User Manual v1.7.3 SPM-MGR-009-001 Rev 5.10

    2. van Winkel SL, Rodríguez-Ruiz A, Appelman L, et al. Impact of artificial intelligence support on accuracy and reading time in breast tomosynthesis image interpretation: a multi-reader multi-case study. Eur Radiol. 2021;31(11):8682-8691.

    3. Arnold M, Morgan E, Rumgay H, et al. Current and future burden of breast cancer: Global statistics for 2020 and 2040. Breast. 2022;66:15-23.

    4. Rodriguez-Ruiz A, Krupinski E, MordangJJ, et al. Detection of breast cancer with mammography: effect of artificial intelligence support system. Radiology. 2019;290(2):305-14.

    5. Pinto MC, Rodriguez-Ruiz A, Pedersen K, et al. Impact of Artificial Intelligence Decision Support Using Deep Learning on Breast Cancer Screening Interpretation with Single-View Wide-Angle Digital Breast Tomosynthesis. Radiology. 2021;300(3):529-536.

    6. Elías Cabot E, Romero Martin S, Raya Povedano J L, Gubern- Mérida A, Álvarez Benito M. Evaluation of the performance of artificial intelligence (AI) after the first six months of use in breast cancer screening practice: Is the promise being delivered? ECR 2022.

    7. Lauritzen AD, Rodríguez-Ruiz A, von Euler-Chelpin MC, et al. An Artificial Intelligence-based Mammography Screening Protocol for Breast Cancer: Outcome and Radiologist Workload. Radiology. 2022;304(1):41-49.

    8. Raya-Povedano JL, Romero-Martín S, Elías-Cabot E, Gubern-Mérida A, Rodríguez-Ruiz A, Álvarez-Benito M. AI-based Strategies to Reduce Workload in Breast Cancer Screening with Mammography and Tomosynthesis: A Retrospective Evaluation. Radiology. 2021;300(1):57-65.

      Currently not available within the Calantic Viewer.

      References

      1. Transpara 510K K210404

      2. van Winkel SL, Rodríguez-Ruiz A, Appelman L, et al. Impact of artificial intelligence support on accuracy and reading time in breast tomosynthesis image interpretation: a multi-reader multi-case study. Eur Radiol. 2021;31(11):8682-8691.

      3. Arnold M, Morgan E, Rumgay H, et al. Current and future burden of breast cancer: Global statistics for 2020 and 2040. Breast. 2022;66:15-23.

      4. Rodriguez-Ruiz A, Krupinski E, MordangJJ, et al. Detection of breast cancer with mammography: effect of artificial intelligence support system. Radiology. 2019;290(2):305-14.

      5. Pinto MC, Rodriguez-Ruiz A, Pedersen K, et al. Impact of Artificial Intelligence Decision Support Using Deep Learning on Breast Cancer Screening Interpretation with Single-View Wide-Angle Digital Breast Tomosynthesis. Radiology. 2021;300(3):529-536.

      6. Elías Cabot E, Romero Martin S, Raya Povedano J L, Gubern- Mérida A, Álvarez Benito M. Evaluation of the performance of artificial intelligence (AI) after the first six months of use in breast cancer screening practice: Is the promise being delivered? ECR 2022.

      7. ScreenPoint Medical Transpara 1.6.0 DBT/3D FDA 510k clearance K193229.

      8. Lauritzen AD, Rodríguez-Ruiz A, von Euler-Chelpin MC, et al. An Artificial Intelligence-based Mammography Screening Protocol for Breast Cancer: Outcome and Radiologist Workload. Radiology. 2022;304(1):41-49.

      9. Raya-Povedano JL, Romero-Martín S, Elías-Cabot E, Gubern-Mérida A, Rodríguez-Ruiz A, Álvarez-Benito M. AI-based Strategies to Reduce Workload in Breast Cancer Screening with Mammography and Tomosynthesis: A Retrospective Evaluation. Radiology. 2021;300(1):57-65.

      What Customers Are Saying

       

      The application enables identification of small nodules faster and more reliably, particularly for oncological patients.

       

      Terrence Matalon
      MD, Chairman, Diagnostic Radiology at Einstein Medical Center
       

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