The PennZone

LOS ALTOS, Calif. - PennZone -- QView Medical, a breast cancer detection pioneer and maker of the FDA-approved artificial intelligence (AI) software system for breast cancer screening, and GE Healthcare today announced that QView's QVCAD software can be launched on GE Healthcare's Invenia™ ABUS 2.0 (Automated Breast Ultrasound), which has an open platform technology that allows the integration of third-party AI tools for the optimization of the reading workflow.

Invenia ABUS 2.0 is the first FDA-approved Ultrasound supplemental breast screening technology specifically designed for detecting cancer in dense breast tissue. The powerful AI Assistant leverages intelligent algorithms to assist in detecting breast lesions, helping physicians increase their reading speed with confidence.

Following years of development, QVCAD, which was previously connected to the Invenia ABUS 2.0 through an external PC and server, is now allowing clinicians to access the software through the Invenia ABUS Viewer, streamlining clinical workflow with reducing reading time by up to 33%¹, while improving clinical outcomes with experience up to 93% sensitivity for lesion detection².

Mammography misses one third of cancers in dense breasts³, and while supplemental screening finds more cancers, it requires reasonable callback and biopsy rates⁴.  Dense breasts increase cancer risk by 4-6 times⁵, affecting more than 40 percent of women³. Early cancer detection is critical to increased survival rates and improved patient care and can reduce the cost of care⁶.

More on The PennZone

• Crossroads4Hope Launches Blood Cancer Resource Hub
• Wilson Venture Capital to Launch News Organization; Will Name Trevor Wilson CEO
• NIL Club Data Shows Brands Are Looking Beyond Follower Counts in College Sports
• Georgia's Magical Nights of Lights Bigger and Brighter for 2026
• Bicentennial Occult Outing Alternative History Video Series Launches for Spooky Season

"ABUS combined with QVCAD provides the next crucial step in supplemental breast screening for women with dense breasts," said Bob Wang, Chairman, and CEO of QView Medical.

Brian McEathron, Vice President, General Imaging Ultrasound at GE Healthcare, said, "Our goal is to support clinicians by enabling the timely, accurate diagnosis and treatment of cancer in dense breasts.  The integration of QVCAD into the ABUS reading process is a breakthrough for streamlining workflow and enhancing clinical confidence.  Identifying breast cancer at the earliest possible stage has important prognostic implications:  a greater number of available treatment options, increased survival rates and an improved quality of life."

QView supports the #AMIDENSE public health effort to raise awareness of breast density as a major risk factor.

About QView Medical, Inc.
QView Medical develops and sells AI image-analysis systems for the reading of 3D Automated Breast Ultrasound Systems (ABUS) The QView team has been the leader AI technologies for over twenty-five years including the development of R2 mammography CAD, R2 lung CT CAD, U-Systems ABUS and QView AI/QVCAD.  QView is located in Los Altos, California.  Visit our website www.qviewmedical.com for more information.

More on The PennZone

• Harvard Educated Board Certified Endodontist Joins Pearlfection Dentistry
• NYC Banners Delivers High-Stakes UN General Assembly Backdrop for the US Department of State Event
• OneVizion Appoints Energy Infrastructure Executive Walt Farrell to Board of Directors
• Tampa Bay Reporter Launches with a Mission to Deliver Local News with National Reach
• "JACKIE the STRIPPER," Starring Charlotte Kirk and The PUSSYCAT DOLLS' Kimberly Wyatt, Now Streaming on PRIME

--

1. Interpretation Time Using a Concurrent-Read Computer-Aided Detection System for Automated Breast Ultrasound in Breast Cancer Screening of Women With Dense Breast Tissue (Yulei Jiang). Read More: https://www.ajronline.org/doi/10.2214/AJR.18.19516
2. Performance and Reading Time of Automated Breast US with or without Computer-aided Detection. Read More: https://pubs.rsna.org/doi/10.1148/radiol.2019181816
3. Pisano et al. Diagnostic Performance of Digital versus Film Mammography
4. Mandelson et al. Journal Natl Cancer Inst 2000; 92:1081–1087 for Breast –Cancer Screening. NEJM 2005; 353:1773
5. Boyd, et al, NEJM Jan 2007
6. Blumen, et al. Comparison of treatment costs for breast cancer by tumor stage and type of service