Machine Learning for Automated Mitral Regurgitation Detection from
  Cardiac Imaging
Ke Xiao Erik Learned-Miller Evangelos Kalogerakis James Priest Madalina Fiterau
Mitral regurgitation (MR) is a heart valve disease with potentially fatal
consequences that can only be forestalled through timely diagnosis and
treatment. Traditional diagnosis methods are expensive, labor-intensive and
require clinical expertise, posing a barrier to screening for MR. To overcome
this impediment, we propose a new semi-supervised model for MR classification
called CUSSP. CUSSP operates on cardiac imaging slices of the 4-chamber view of
the heart. It uses standard computer vision techniques and contrastive models
to learn from large amounts of unlabeled data, in conjunction with specialized
classifiers to establish the first ever automated MR classification system.
Evaluated on a test set of 179 labeled -- 154 non-MR and 25 MR -- sequences,
CUSSP attains an F1 score of 0.69 and a ROC-AUC score of 0.88, setting the
first benchmark result for this new task.