DiA intends to develop its automated imaging analysis technology to work with GE Healthcare's ultrasound devices.

DiA has marketed its automated imaging analysis technology to deliver immediate, accurate, and reproducible imaging interpretation of ultrasound for point of care settings.

These tools utilize advanced, proprietary pattern recognition and sophisticated machine learning algorithms that can dramatically improve monitoring of patient conditions, offering physicians powerful tools to support their decisions.

"We are pleased to partner with DiA Imaging Analysis. GE Healthcare has a long history of bringing innovative solutions to our customers around the world," Rob Walton, general manager of GE Healthcare Primary & Affordable Care commented. "These tools would bring new capabilities to ultrasound at the point of care." 

Hila Goldman Aslan, DiA's Chief Executive Officer and Co-Founder, stated, "We are excited with this collaboration, and look forward to working with a market leader such as GE Healthcare.  Our advanced automated tools for point of care echo ultrasound analysis are first to market, and we are planning to soon launch additional automated imaging analysis tools as part of our vision to improve patient care."

Arnon Toussia-Cohen, DiA's Chairman and Vice President of Business Development, added.  "To date, we are the only company that offers automated tools for handheld ultrasound devices and we are proud to partner with GE Healthcare to provide immediate information at the point of care."

The Company will be showing its systems at the upcoming Radiological Society of North America (RSNA) meeting in Chicago at the Machine Learning Showcase,booth #8545.

DiA Imaging Analysis is a medical imaging analysis software company providing fully automated, implementable tools that enable quick, objective, and accurate imaging analysis, with an initial focus on echocardiography.

The Company's FDA and CE cleared cognitive image processing tools are based on advanced pattern recognition and machine learning algorithms that automatically imitate the way the human eye identifies borders and motion, producing accurate and reliable data for the use of physicians.