Eko, a Berkeley, California-based health tech company that employs artificial intelligence and machine learning to monitor and manage cardiovascular diseases, as per recent reports, has introduced an investigational Aortic Stenosis (AS) detection algorithm at the American Society of Echocardiography (ASE) 2019 Scientific Sessions. The AS detection algorithm will enable clinical providers to early detect the clinically significant AS. With this new algorithm, Eko, following its mission, has taken a major step forward in enabling healthcare providers with the necessary tools to accurately detect structural heart disease during primary care visits.
In the US alone, there are three million patients, who suffer from Aortic Stenosis – a common structural heart disease – and estimations are that the disease may likely affect more than 1 in 8 patients over the age of 75, globally. The conventional way to identify the disease is a physical exam, where a healthcare provider uses the stethoscope to hear the heart murmur. However, following the subjectivity involved in using a stethoscope, and the difficulty to identify the subtle AS murmur, the disease often goes unnoticed in symptomatic patients.
The early results of the AS detection algorithm, developed by Eko in association with the Northwestern Medicine Bluhm Cardiovascular Institute, showed that Eko’s AI was able to accurately detect AS in a cohort of Aortic Stenosis patients with a sensitivity of 97.2% and a specificity of 86.4%. In their conclusion, the Northwestern researchers have made the assessment that Eko’s platform is a fast and effective method to screen for significant AS and should be validated in a primary care setting.
Eko, continuing its partnership with the Bluhm Cardiovascular Institute at Northwestern, will be rigorously testing the AS screening algorithm, before submitting the algorithm for regulatory clearance.