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HEALTHCARE | Medical Devices & Equipment / Imaging & Diagnostic Equipment
qviewmedical.com

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Founded Year

2006

Stage

Unattributed | Alive

Total Raised

$5.32M

Last Raised

$2M | 5 yrs ago

About QView Medical

QView Medical is an early-stage medical imaging software company located in Los Altos, California. The company is developing a Computer-Aided-Detection (CAD) software for 3D Breast Ultrasound screening systems. QView is in the process of integrating and testing it's 3D Breast Ultrasound CAD platform with leading physicians in this field.

QView Medical Headquarter Location

4546 El Camino Real Suite 215

Los Altos, California, 94022,

United States

650-397-5174

Latest QView Medical News

QView Medical Showcases FDA Approved QVCAD First AI System for Concurrent Reading of ABUS Exams at RSNA

Nov 15, 2018

QView Medical will showcase QVCAD, the first AI CAD system FDA -Approved for concurrent reading of Automated Breast Ultrasound (ABUS) exams, at the upcoming 104th Annual Radiological Society of North America (RSNA) meeting, November 25-30, 2018 (South Hall #3572). Being demonstrated for the first time since being approved, QVCAD reduces interpretation time of screening ABUS exams while maintaining diagnostic accuracy. In addition, QView successfully completed an installation of QVCAD at Northern Arizona Healthcare, the first joint installation with the Invenia™ ABUS system from GE Healthcare, approved by the FDA for breast cancer screening as an adjunct to mammography for asymptomatic women with dense breasts. “We are excited to add the capability to offer ABUS as part of our comprehensive breast cancer screening program, which will help improve cancer detection for women with dense breasts. However, it was important that we implement with concurrent decision support from QVCAD to improve reading times while maintain workflow and diagnostic confidence,” said Liz Palomino, MHA, RT, Northern Arizona Healthcare Director of Medical Imaging, Flagstaff Medical Center, Sedona Medical Center, Verde Valley Medical Center and Verde Valley Medical Imaging Center. Results from several clinical studies have shown that the addition of ABUS to screening mammography results in a significant increase in cancer detection in women with dense breasts. However, the interpretation of ABUS exams, with up to 2,000 images per case, is complex and time consuming, particularly for new users. According to results of a reader study published recently in the American Journal of Radiology, QVCAD reduced reader interpretation time by 33 percent while maintaining diagnostic accuracy. Based on deep learning algorithms, the AI system is designed to detect suspicious areas of breast tissue that have characteristics similar to breast lesions and highlight suspicious area to distinguish potentially malignant lesions from normal breast tissue. QVCAD is FDA Approved for use with ABUS systems and has received the CE Mark for use with ABUS/ABVS systems. To improve reader productivity, QVCAD provides synthetic 2D images of all six volumetric datasets in a standard ABUS exam to provide an immediately visual overview of the case. The C-thru images, which are minimum intensity projections (MinIP), summarize each 3D ABUS volume in a 2D image and bring attention to specific areas of interest by enhancement of radial spiculations and retraction patterns in coronal reconstructions, which are highly suggestive of breast cancer in ABUS. Users may select any CAD mark or area of interest on the C-thru image and the corresponding original ABUS images will be displayed, enabling users to efficiently review the entire ABUS case. “The QVCAD system is intended to serve as a review and navigation tool designed to improve reader productivity while preserving the accuracy of diagnosis. We believe that QVCAD will be an important tool in reducing the learning curve for new ABUS users and increasing their confidence in interpreting ABUS exams, which will be key to continued adoption of this important adjunctive screening modality,” said Bob Wang, QView Medical CEO. About QView Medical QView Medical, incorporated in 2006, developed its deep learning AI-CAD for automated breast ultrasound systems (ABUS). The QVCAD system is FDA approved and can now be used with any currently installed GE Invenia system. For more information, visit www.qviewmedical.com View source version on businesswire.com: https://www.businesswire.com/news/home/20181115005326/en/ Chris K. Joseph

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Expert Collections containing QView Medical

Expert Collections are analyst-curated lists that highlight the companies you need to know in the most important technology spaces.

QView Medical is included in 4 Expert Collections, including Women's Health & Wellness.

W

Women's Health & Wellness

958 items

Startups focused on providing products and services catering to women's health and wellbeing.

M

Medical Devices

8,366 items

Companies developing medical devices (per the IMDRF's definition of "medical device"). Includes software, lab-developed tests (LDTs), and combination products. *Columns updated as regularly as possible.

H

Health Monitoring & Diagnostics

2,706 items

Companies developing or offering products that aid in the assessment, screening, diagnosis, or monitoring of a person's state of health/wellness. Excludes companies focused solely on fitness/sports performance

D

Digital Health

11,262 items

Technologies, platforms, and systems that engage consumers for lifestyle, wellness, or health-related purposes; capture, store, or transmit health data; and/or support life science and clinical operations. (DiME, DTA, HealthXL, & NODE.Health)

QView Medical Patents

QView Medical has filed 7 patents.

The 3 most popular patent topics include:

  • Habitat management equipment and methods
  • Breast cancer
  • Breast diseases
patents chart

Application Date

Grant Date

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9/27/2017

3/31/2020

Medical imaging, Medical ultrasonography, Breast cancer, Medical equipment, Habitat management equipment and methods

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3/31/2020

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Related Topics

Medical imaging, Medical ultrasonography, Breast cancer, Medical equipment, Habitat management equipment and methods

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Status

Grant

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