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Eko study will use AI to identify heart problems

March 8, 2019 By Nancy Crotti

Eko’s Duo digital stethoscope (Image from Eko)

A new study will investigate whether Eko’s digital stethoscopes and artificial intelligence (AI) algorithms can interpret heart sounds accurately to help screen for pathologic heart murmurs and valvular heart disease.

Eko’s Duo portable cardiac device won FDA clearance in 2017. The hand-held system combines a digital stethoscope with an electrocardiogram to give cardiologists insight into their patients’ daily cardiac function, including remote monitoring via Eko’s smartphone app.

The traditional stethoscope remains the go-to device for heart disease screening but requires precise interpretation of abnormal heart sounds by cardiologists. Machine learning can combine the data from tens of thousands of heart sound patterns and provide that accuracy to physicians anywhere, according to a statement from Northwestern Medicine Bluhm Cardiovascular Institute in Chicago. The institute plans to enroll 800 of the 1,000 patients Eko is seeking for the study.

The trial, which Eko is funding, is part of the institute’s new Center for Artificial Intelligence, where Northwestern’s cardiovascular clinical program works with early innovators in AI, develops new products, and trains physicians at the university’s McCormick School of Engineering and Applied Science’s master’s program in artificial intelligence.

“One of the biggest problems in healthcare is that general practitioners so often miss heart murmurs that if found earlier would allow patients to get treatment before problems arise,” said Eko CEO Connor Landgraf in the statement. “Northwestern is known for their work in valvular heart diseases and together we are working on AI technology that will detect the two most prevalent valve diseases with the sensitivity of a cardiologist. The results of this study will work to bridge the gap for general practitioners and early detection of these life-threatening conditions.”

“If proven effective, Eko’s platform could be a much simpler, lower cost way to identify patients with heart disease,” added James Thomas, M.D. director of the Northwestern Center for Heart Valve Disease and the principal investigator for the study at Northwestern. “We are looking to support and advance work that broadens access to the best diagnostic tools in healthcare, regardless of whether a patient lives in the city or a more rural area. Deep learning provides that expert knowledge, regardless of a patient’s location.”

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