Machine Learning for Automated Mitral Regurgitation Detection from Cardiac Imaging

Ke Xiao, Erik G. Learned-Miller, Evangelos Kalogerakis, James Priest, Madalina Fiterau

Abstract: 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 magnetic resonance (CMR) 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 using CMR imaging sequences. 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 detecting MR from CMR imaging sequences.