Eko is using a $2.7 million small business innovation research grant from the National Institutes of Health to design a machine learning algorithm that would detect the condition using the company’s smart stethoscopes. The method will employ data from phonocardiograms and electrocardiograms and is designed to be less invasive and costly than the current standard of EKG and right-heart catheterization.
“This machine learning algorithm has the potential to be a low cost, easily implementable, and sustainable medical technology that assists healthcare professionals in identifying more patients with pulmonary hypertension,” said Dr. Gaurav Choudhary, director of cardiovascular research at the Warren Alpert Medical School of Brown University and Lifespan Cardiovascular Institute, in the news release.
Eko has received $6 million from NIH to date to develop cardiopulmonary AI tools and has previously collaborated with Chicago-based Northwestern Medicine.
“This is how we change the standard of cardiac care,” stated Eko co-founder and CEO Connor Landgraf.
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