  The left ventricular of ejection fraction is one of the most important metric
of cardiac function. It is used by cardiologist to identify patients who are
eligible for lifeprolonging therapies. However, the assessment of ejection
fraction suffers from inter-observer variability. To overcome this challenge,
we propose a deep learning approach, based on hierarchical vision Transformers,
to estimate the ejection fraction from echocardiogram videos. The proposed
method can estimate ejection fraction without the need for left ventrice
segmentation first, make it more efficient than other methods. We evaluated our
method on EchoNet-Dynamic dataset resulting 5.59, 7.59 and 0.59 for MAE, RMSE
and R2 respectivelly. This results are better compared to the state-of-the-art
method, Ultrasound Video Transformer (UVT). The source code is available on
https://github.com/lhfazry/UltraSwin.
