Zebra Medical debuts two machine learning algorithms to predict heart disease risk

human heart

Machine learning player Zebra Medical Vision has come up with two new algorithms that can help predict patients’ risk of cardiovascular events. Applying them to routine CT scans could help physicians identify high-risk patients early enough to ward off cardiovascular disease and fatty liver, which are both underdiagnosed.

The first algorithm quantifies the amount of calcification or plaque in coronary arteries, which can predict the risk of cardiovascular events, such as heart attack or stroke. The second identifies fatty liver in a patient’s CT scans, offering information about the patient’s metabolic state. If fatty liver is caught early enough, steps can be taken to prevent its progressing to cirrhosis, chronic liver disease or liver cancer.

While the algorithms are useful on their own, they are particularly effective when used together, Zebra Medical said in a statement. Research shows that fatty liver is associated with a two to four times elevated risk of high-risk coronary artery plaque that can lead to a heart attack.

“This is yet another step in our mission to help provide faster, more accurate radiology services at lower cost, by teaching software to read and identify key clinical conditions in imaging,” said Zebra CEO Elad Benjamin in the statement. “We believe that these tools, as well as new algorithms which we continuously release, will help Radiologists deal with the continuous pressure they face to increase output and maintain high quality of care.”

Zebra entered a multiyear partnership with Dell Services in November, under which the tech giant will offer Zebra’s analytical tools to clinical sites for screening and diagnostic decision support. And in March, the duo announced that they would collaborate to bring algorithm-based image analysis to healthcare providers. And in May, Zebra Medical raised $12 million in financing, led by Salt Lake City’s not-for-profit Intermountain Healthcare.

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