  Ejection fraction (EF) is a key indicator of cardiac function, allowing
identification of patients prone to heart dysfunctions such as heart failure.
EF is estimated from cardiac ultrasound videos known as echocardiograms (echo)
by manually tracing the left ventricle and estimating its volume on certain
frames. These estimations exhibit high inter-observer variability due to the
manual process and varying video quality. Such sources of inaccuracy and the
need for rapid assessment necessitate reliable and explainable machine learning
techniques. In this work, we introduce EchoGNN, a model based on graph neural
networks (GNNs) to estimate EF from echo videos. Our model first infers a
latent echo-graph from the frames of one or multiple echo cine series. It then
estimates weights over nodes and edges of this graph, indicating the importance
of individual frames that aid EF estimation. A GNN regressor uses this weighted
graph to predict EF. We show, qualitatively and quantitatively, that the
learned graph weights provide explainability through identification of critical
frames for EF estimation, which can be used to determine when human
intervention is required. On EchoNet-Dynamic public EF dataset, EchoGNN
achieves EF prediction performance that is on par with state of the art and
provides explainability, which is crucial given the high inter-observer
variability inherent in this task.
