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DiA is fixing the major problem with ultrasound: Human error

DiA are aiming to help medical professionals to ensure that their ultrasound images can finally be captured correctly, and more efficiently. DiA is a market leader provider of artificial intelligence-based solutions for generating ultrasound images and the subsequent analysis.

The new technology sets out to solve the two main challenges clinicians are facing when using ultrasound. The first is with how to acquire the right image; and the second issue is with how to analyze the image correctly

Ultrasound involves the use of sound waves with frequencies higher than the upper audible limit of human hearing. Also called sonogram, the technology is used in the medical field to bounce soundwaves off tissues to form images. The technology is similar to that used by sonar and radar, which help the military detect planes and ships.

In terms of turning the technology to scale, DiA has already raised $12.6 million in funding, received four European and U.S. Food and Drug Administration (FDA) clearances. In addition, the firm has partnered with nine leading channels, including GE Healthcare, IBM, Konica Minolta and more.

DiA’s artificial intelligence based solution uses advanced pattern recognition and machine learning algorithms that imitate the way the human eye identifies borders and motions in ultrasound images.

Through the technology, the DiA system guides clinicians to capture the appropriate image, and then to objectively analyze it. The focus is with detecting clinical abnormalities that can be easily missed using visual analysis.

To date, DiA’s software is used by over 1000 end users and sold in over 20 countries – addressing a market of 8,000 hospitals and over 18 million clinicians in the U.S. The company has recently announced its collaboration with IBM Watson Health. IBM Watson will offer DiA’s LVivo EF solution – an AI-powered and CE marked and FDA cleared cardiac ultrasound software.

DiA also has an expanding list of solutions currently offers different algorithms for cardiology.

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Written By

Dr. Tim Sandle is Digital Journal's Editor-at-Large for science news. Tim specializes in science, technology, environmental, business, and health journalism. He is additionally a practising microbiologist; and an author. He is also interested in history, politics and current affairs.

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