Abstract:
Rheumatic Heart Disease is a cardiovascular disease highly prevalent in
developing countries partially because of inadequate healthcare infrastructure to treat
Group A streptococcus pharyngitis and thereafter diagnose and document every case
of Acute Rheumatic Fever, the immune-mediated antecedent of rheumatic heart
disease. Secondary antibiotic treatment with penicillin injections after a diagnosis of
Acute Rheumatic Fever and Rheumatic Heart Disease is used to prevent further
attacks of Strep A, preferably prior to any heart valve damage. Echocardiographic
screening for early detection of Rheumatic Heart Disease has been proposed as a
method to improve outcomes but it is time-consuming, costly and few people are
skilled enough to reach a correct diagnosis. Machine Learning is an emerging tool in
analysing medical images; our aim is to automate the screening process of
diagnosing rheumatic heart disease. In this paper, we present a web application to be
used to label echocardiography data. These labelled data can then be used to develop
machine learning models that can classify echocardiographic views of the heart and
damaged valves from the echocardiograms.
