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Large clinical study highlights Eko Health's AI for early detection of pulmonary hypertension

Press releases may be edited for formatting or style | February 10, 2025 Business Affairs
SAN FRANCISCO, Feb. 6, 2025 /PRNewswire/ -- Eko Health, a leader in applying artificial intelligence (AI) for the early detection of heart and lung diseases, today announced publication of a peer-reviewed study evaluating its novel algorithm for the detection of pulmonary hypertension (PH). The study, which was published in the Journal of the American Heart Association (JAHA), highlighted the algorithm's ability to analyze heart sounds recorded with a digital stethoscope for identifying elevated pulmonary artery systolic pressures, a key indicator of PH.

The study underscores the potential of this non-invasive, rapid detection tool to aid clinical decision-making in primary care and other settings where costly or invasive diagnostic methods are less accessible. Additionally, the algorithm demonstrated its ability to pinpoint specific, clinically relevant segments of heart sound recordings, offering a transparent and explainable AI approach that aligns with physicians' diagnostic workflows.

"This innovative approach demonstrates how combining digital stethoscopes with advanced AI can lead to a low-cost, non-invasive, point-of-care screening tool for the early detection of pulmonary hypertension," said Dr. Gaurav Choudhary, Lead Principal Investigator, and Ruth and Paul Levinger Professor of Cardiology and Director of Cardiovascular Research at Brown University Health and the Alpert Medical School of Brown University. "Our findings represent a significant advancement in clinical practice that can ultimately enhance patient care."

The study utilized 6,000 heart sound recordings paired with echocardiographic pressure estimates to train the AI model. The algorithm demonstrated strong performance, with an average area under the receiver operating characteristic (AUROC) curve of 0.79, a sensitivity of 71%, and a specificity of 73%. Ongoing data collection from over 1,200 patients continues to refine the model's accuracy and clinical utility, with the goal of further improving detection capabilities for broader clinical use.

"These encouraging results highlight Eko Health's unwavering commitment to advancing innovation in cardiopulmonary health," said Dr. Steve Steinhubl, Chair of Eko's Scientific Advisory Board and Cardiologist and Professor of Biomedical Engineering at Purdue University. "The company's goal is to develop pioneering AI solutions that address significant gaps in healthcare delivery. Early detection and intervention are essential in addressing cardiovascular diseases, and Eko is dedicated to providing accessible and scalable technologies that empower healthcare providers while improving patient care globally."

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