Rheumatic Heart Disease (RHD) is a cardiovascular disease common in low and middle income countries due to inadequate infrastructure to treat Group A strep throat infections. RHD is caused by damage to heart valves due to inflammation and scarring caused by repeat rheumatic fever episodes. Rheumatic fever is caused by an autoimmune body response to the Group A strep throat infection caused by Group A streptococcal bacteria. It mostly affects children in developing countries where poverty is widespread, and the bacteria can easily spread. The mitral and aortic valves, found on the left side of the heart, are mostly affected by RHD.
It is important to detect cardiovascular diseases early so that management of the disease can begin. Echocardiographic screening for early detection of RHD has been proposed as a method to screen for RHD, but it is time consuming, costly and only a few people are skilled enough to reach a correct diagnosis.
In this research project we are presenting the use of machine learning as a tool to analyze echocardiograms which will automate the screening process of diagnosing RHD. The World Heart Federation (WHF) has come up with guidelines/ criteria for subclinical disease for RHD in asymptomatic populations. The criteria include valve thickness as a morphological criterion. Another criterion used is the velocity of the mitral valve’s regurgitant jet and length in at least two views, including the parasternal long axis view (PLAX).
So far, an echocardiographic view classifier has been developed. It is able to classify an echo with a PLAX view and a NOT PLAX view. A web application based on Dash has also been developed. This has been used by cardiologists to label data of echocardiograms we have, which will provide data to train and test a multiclass echocardiographic view classifier and a valve damage classifier.
With the labelled data from the web app, we will develop a multiclass echocardiographic view classifier and a valve damage classifier.