iCAD to Participate in the Guggenheim MedTech Disruptors Summit
NASHUA, N.H.,
Aug. 10, 2020 (GLOBE NEWSWIRE) — iCAD, Inc. (NASDAQ: ICAD), a global medical technology
leader providing innovative cancer detection and therapy solutions, today
announced that Michael Klein, Chairman and Chief Executive Officer, will
present a corporate overview at the Guggenheim MedTech Disruptors Summit,
taking place virtually on August 10-11, 2020.
Presentation
Details
Date: August
11, 2020
Time: 3:00pm
Eastern Time
About
iCAD, Inc.
Headquartered
in Nashua, NH, iCAD is a global medical technology leader providing innovative
cancer detection and therapy solutions.
ProFound AI™
is a high-performing workflow solution for 2D and 3D mammography, or digital
breast tomosynthesis (DBT), featuring the latest in deep-learning artificial
intelligence. In 2018, ProFound AI for Digital Breast Tomosynthesis (DBT)
became the first artificial intelligence (AI) software for DBT to be
FDA-cleared; it was also CE marked and Health Canada licensed that same year.
It offers clinically proven time-savings benefits to radiologists, including a
reduction of reading time by 52.7 percent, thereby halving the amount of time
it takes radiologists to read 3D mammography datasets. Additionally, ProFound
AI for DBT improved radiologist sensitivity by 8 percent and reduced
unnecessary patient recall rates by 7.2 percent.i
The Xoft
System is FDA-cleared, CE marked and licensed in a growing number of countries
for the treatment of cancer anywhere in the body. It uses a proprietary
miniaturized x-ray source to deliver a precise, concentrated dose of radiation
directly to the tumor site, while minimizing risk of damage to healthy tissue
in nearby areas of the body.
For more
information, visit www.icadmed.com and www.xoftinc.com.
Contacts:
Media inquiries:
Jessica Burns, iCAD
+1-201-423-4492
jburns@icadmed.com
Investor
Relations:
Jeremy Feffer, LifeSci Advisors
+1-212-915-2568
i Conant, E. et al. (2019). Improving Accuracy and Efficiency with
Concurrent Use of Artificial Intelligence for Digital Breast Tomosynthesis.
Radiology: Artificial Intelligence. 1 (4). Accessed via https://pubs.rsna.org/doi/10.1148/ryai.2019180096