AI startup DiA Imaging Analysis raises $5M to grow business, expand portfolio

DiA Imaging Analysis, a Be'er Sheva, Israel-based startup focused on artificial intelligence (AI) and medical imaging, announced this week that it has closed a $5 million round of funding. 

DiA Imaging Analysis collaborates with leading healthcare vendors and builds automation tools used in ultrasound devices, IT systems and cloud-based platforms. This latest round of funding is expected to help the company expand its portfolio and build “support teams” in the United States, Europe and Asia.

The funding round was led by Connecticut Innovations, though Defta Healthcare and existing investors also contributed.  

“The medical imaging analysis market is expanding rapidly, while the need for automation is increasing,” Hila Goldman Aslan, DiA co-founder and CEO, said in a prepared statement. “DiA’s innovative technology and market traction will continue bringing cutting-edge solutions to the market to improve patient care. I welcome our new investors and thank them, along with our existing investors, for their support in our vision to lead the ultrasound imaging analysis market.”

“The team at DiA Imaging Analysis continues to make great strides in bringing to market their groundbreaking ultrasound analysis technology, powered by artificial intelligence, that improves patient care,” Peter Longo, senior managing director for investments at Connecticut Innovations, said in the same statement. “We are excited about their prospects for success as we continue to support their commercialization activities.”

Michael Walter
Michael Walter, Managing Editor

Michael has more than 18 years of experience as a professional writer and editor. He has written at length about cardiology, radiology, artificial intelligence and other key healthcare topics.

Around the web

The ACR hopes these changes, including the addition of diagnostic performance feedback, will help reduce the number of patients with incidental nodules lost to follow-up each year.

And it can do so with almost 100% accuracy as a first reader, according to a new large-scale analysis.

The patient, who was being cared for in the ICU, was not accompanied or monitored by nursing staff during his exam, despite being sedated.