Just recently, researchers have made some considerable progress in developing artificial intelligence detectors that can diagnose and detect a disease related to heart. Only with a use of a compact wrist band biosensor, these AI classifier can perform their work efficiently to diagnose this condition called Hypertrophic Cardiomyopathy. This condition usually goes unnoticed in clinical set ups but a wearable sensor equipped machine learning diagnostic abilities can prove to be really helpful in detecting it as they provide a very simple, noninvasive and easily accessible solution for this problem. The work of these researchers was published on June 24 in NPJ Digital Medicine.
Hypertrophic Cardiomyopathy is one of the cardiovascular diseases that sometimes flies under the radar and gets a little hard to spot. Characteristically in this conditions, cardiovascular muscles thicken abnormally leading to risks of many serious conditions like heart stroke, heart failure and even sudden cardiac death. The worst of this condition appears in those who have an obstruction in outflow tract. It’s usually termed as oHCM. It accounts for more than thirty percent of the patients having HCM. Forward blood experiences an obstruction in its fluid movement.
Researchers working on this condition pursued a fresh means of detecting Hypertrophic Cardiomyopathy with the help of Artificial Intelligence (AI) and biosensor that could be worn around the wrist of the user. This biosensor works with a technique called photoplethysmography (PPG). Photoplethysmography (PPG) detects any slight changes in the volume of the blood of user on the surface of the skin by a very noninvasive manner. It is also used in clinical pulse oximeters. Smartwatches also use this technique to monitor and track heart rates. As the smartwatches and wristbands have become a real commodity these days, the researches came up with an idea of putting PPG and machine learning technologies together to produce a very efficient way of diagnosing Hypertrophic Cardiomyopathy.