Rather than relying on patient presentation of symptoms and subsequent echocardiograms, this platform feeds low-cost electrocardiogram measurements to a trained deep learning algorithm which outputs patient diagnosis.
This technology is a diagnostic tool combining electrocardiography with the power of deep learning algorithms to detect moderate to severe aortic stenosis, aortic regurgitation, and mitral regurgitation.
This technology also consists of a self-calibrating algorithm that negates the need of regular calibration of cuffless blood pressure monitors, which uses information received from biometric and environmental sensors to regularly correct blood pressure estimates.
This technology furthermore describes a position-tracking algorithm which corrects hydrostatic pressure errors common in wearable wrist devices due to changes in arm position and movement using a combination of positional sensors and machine-learning algorithms.
This technology furthermore describes a position-tracking algorithm which corrects hydrostatic pressure errors common in wearable wrist devices due to changes in arm position and movement using a combination of positional sensors and machine-learning algorithms.
This technology predicts a patient’s risk of developing primary graft dysfunction (PGD) after a heart transplant by measuring levels of exosome proteins such as kallikrein (KLKB1), to improve risk stratification, organ allocation, and post-operative care.