Cutting-Edge Screening & diagnosTIC CAPABILITIES
Using machine learning, CPS can be trained to almost any standard hemodynamic measurement and methodology.
In addition to providing objective measurements on critical heart functions, future iterations of the CPS will use these measures to automatically detect cardiac abnormalities and rapidly screen for prevalent heart conditions without the need for operator interpretation.
Proof-of-concept clinical results have been published for the automated detection of aortic stenosis and ventricular dysfunction using the CPS technology, and we're continuing to explore additional indications. These early studies provide a glimpse into the future of cardiovascular medicine, where the all-in-one CPS platform will be used to measure, screen, and diagnose an array of cardiac conditions at the point of care.
April 1, 2022
NON-INVASIVE AND AUTOMATED CARDIAC OUTPUT MEASUREMENT FROM CARDIAC ACOUSTIC SIGNALS USING THE CARDIAC PERFORMANCE SYSTEM: A COMPARISON WITH CATHETERIZATION
Looking for an AI tool in cardiology that can do more than just rule in or out heart disease? Sensydia’s Cardiac Performance System is an automated, non-invasive platform designed to provide gold-standard-level hemodynamic measurements for screening and management of cardiovascular patients. Check out the performance of our Cardiac Output artificial intelligence algorithm presented at the ACC.22 conference! Click on the image to see our poster and on the link below to read our abstract in the Journal of the American College of Cardiology.
April 1, 2022
NOV-INVASIVE AND AUTOMATED MEASUREMENT OF EJECTION FRACTION USING MACHINE LEARNING ALGORITHMS ON CARDIAC ACOUSTIC SIGNALS: A COMPARISON WITH ECHOCARDIOGRAPHY
Measuring cardiac Ejection Fraction without echo? It’s possible!
Sensydia’s Cardiac Performance System is a non-invasive and automated device that can provide gold-standard-level hemodynamic measurements for screening and management of cardiovascular patients.
Check out the performance of our FDA-cleared Ejection Fraction artificial intelligence algorithm presented at the ACC.22 conference! Click on the image to see our poster and on the link below to read our abstract in the Journal of the American College of Cardiology.
August 27, 2020
Assessment of Left Ventricular Diastolic Function USING Phonocardiogram Signals: A Comparison with Echocardiography
This paper compares a fully automated phonocardiogram (PCG) based wearable system with echocardiography for the early evaluation of heart failure patients. When tested on n=34 adult inpatients undergoing right heart catheterization, the system was able to identify left ventricular diastolic dysfunction with 87.5% accuracy and elevated left atrial pressure with 75% accuracy.
March 24, 2020
Phonocardiogram (PCG) and electrocardiogram signals were acquired simultaneously using an automated system of acoustic sensors and electrodes in 18 subjects. Mitral E/A ratio was computed via PCG using a feature-based linear model against doppler echo.Fully-automated PCG-based E/A ratio computation represents a first step towards heart failure screening at the point of primary care.
May 19, 2019
This paper presents an end-to-end, fully automated, non-invasive system that uses noise-subtraction, heartbeat segmentation and quality-assurance algorithms to extract physiologically-motivated features from PCG signals to diagnose Aortic Stenosis (AS). When tested on n=96 patients showing a diverse set of cardiac and non-cardiac conditions, the system was able to diagnose AS with 92% sensitivity and 95% specificity.