M(otion)-mode Based Prediction of Ejection Fraction using Echocardiograms

TL;DR

This paper introduces an automated method for estimating ejection fraction from echocardiograms using M-mode images and contrastive learning, improving efficiency and accuracy especially with limited labeled data.

Contribution

It proposes a novel approach combining M-mode image generation with contrastive learning for cardiac function assessment, reducing reliance on extensive annotations and complex training.

Findings

Supervised models perform well with only ten modes.

Contrastive learning enhances accuracy with limited labeled data.

Method is computationally efficient and bypasses complex training processes.

Abstract

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.