M(otion)-mode Based Prediction of Cardiac Function on Echocardiograms

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), which is used to diagnose 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 both time-consuming and expertise-demanding, raising the need for an automated approach. Earlier automated works have been limited to still images or use echocardiogram videos with spatio-temporal convolutions in a complex pipeline. In this work, we propose to generate images from readily available echocardiogram videos, each image mimicking a M(otion)-mode image from a different scan line through time. We then combine different M-mode images using off-the-shelf model architectures to estimate the EF and, thus, diagnose cardiomyopathy. Our experiments show that our proposed method converges with only ten modes and is comparable to the baseline method while bypassing its cumbersome training process.Keywords: Echocardiography, M-mode Ultrasound, Ejection Fraction