FDA Clears Machine Learning Algorithm for Heart Murmur Detection

The Food and Drug Administration (FDA) has cleared the Eko Murmur Analysis Software, a cloud-based machine learning algorithm, to detect and characterize heart murmurs in adults and pediatric patients.

The Eko Murmur Analysis Software uses signal processing, as well as algorithms derived from machine learning, to analyze heart sound data acquired through Eko’s smart stethoscopes and provide clinical decision support. The software is designed to automatically detect murmurs if present, and the murmur timing and character, including S1, S2, innocent heart murmurs, structural heart murmurs, and the absence of a heart murmur.

The FDA clearance was based on performance data demonstrating that the Eko Murmur Analysis Software is safe and effective at identifying valvular heart disease, with an overall sensitivity of 85.6% and specificity of 84.4%. Moreover, the algorithm identified structural murmurs in adults 18 years of age and older with a sensitivity of 90.2% and specificity of 90.6%.

“This latest FDA clearance is another way in which Eko is improving access to better heart health through clinically-validated algorithms and best-in-class medical devices,” said Connor Landgraf, Co-founder & CEO of Eko. “By making heart disease screening algorithms and digital stethoscopes accessible in exam rooms around the country, we are moving towards a future in which more objective and consistent valvular heart disease screening can become the standard of care.”

The Eko Murmur Analysis Software is not intended as a sole means of diagnosis and is for use in environments where health care is provided by clinicians.

FDA Clears Machine Learning Algorithm to Assist in Heart Murmur Detection

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