News | Breast Imaging | August 02, 2019

Volpara will sell artificial intelligence-powered breast cancer detection solution in the U.S., Australia, New Zealand and parts of Asia

Volpara to Distribute Screenpoint Medical's Transpara AI Solution

August 2, 2019 — Volpara Solutions and ScreenPoint Medical BV signed an agreement under which Volpara will sell ScreenPoint's Transpara products to breast imaging clinics in the United States, Australia, New Zealand and parts of Asia. Transpara is designed to assist radiologists with the reading of mammograms and is one of the first next-generation artificial intelligence (AI) applications for detecting breast cancer in screening mammograms to gain 510(k) clearance from the U.S. Food & Drug Administration (FDA).

FDA clearance was supported by the results of a multi-reader, multi-case reader study published in February 2019 in Radiology, which demonstrated that radiologists using Transpara significantly improved detection accuracy without increasing reading times.1 Radiologists' performance consistently improved independent of their level of experience. In the JNCI publication that followed in March, it was reported that when compared to 101 radiologists, the stand-alone performance (sensitivity and specificity) of Transpara was as accurate.2 This suggests that the system gives an objective second opinion similar to that of a second radiologist. 

Transpara gained European regulatory approval (CE) for use with multi-vendor mammography (2018) and digital breast tomosynthesis (DBT) images (2019), and is already installed at breast imaging centers in Europe.

Both companies showcased their complete suite of breast imaging analytics tools at the Association for Medical Imaging Management (AHRA) 2019 Annual Meeting, July 21-24 in Denver.

For more information: www.volparasolutions.com, www.screenpoint-medical.com

Reference

1. Rodriguez-Ruiz A., Krupinski E., Mordang J., et al. Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System. Radiology, published online Nov. 20, 2018. https://doi.org/10.1148/radiol.2018181371

2. Rodriguez-Ruiz A., Lang K., Gubern-Merida A., et al. Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists. Journal of the National Cancer Institute, March 5, 2019. https://doi.org/10.1093/jnci/djy222

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