M(otion)-mode Based Prediction of Ejection Fraction using
  Echocardiograms
Ece Ozkan Thomas M. Sutter Yurong Hu Sebastian Balzer Julia E. Vogt
Early detection of cardiac dysfunction through routine screening is vital for
diagnosing cardiovascular diseases. An important metric of cardiac function is
the left ventricular ejection fraction (EF), where lower EF is associated with
cardiomyopathy. Echocardiography is a popular diagnostic tool in cardiology,
with ultrasound being a low-cost, real-time, and non-ionizing technology.
However, human assessment of echocardiograms for calculating EF is
time-consuming and expertise-demanding, raising the need for an automated
approach. In this work, we propose using the M(otion)-mode of echocardiograms
for estimating the EF and classifying cardiomyopathy. We generate multiple
artificial M-mode images from a single echocardiogram and combine them using
off-the-shelf model architectures. Additionally, we extend contrastive learning
(CL) to cardiac imaging to learn meaningful representations from exploiting
structures in unlabeled data allowing the model to achieve high accuracy, even
with limited annotations. Our experiments show that the supervised setting
converges with only ten modes and is comparable to the baseline method while
bypassing its cumbersome training process and being computationally much more
efficient. Furthermore, CL using M-mode images is helpful for limited data
scenarios, such as having labels for only 200 patients, which is common in
medical applications.