Learning spatiotemporal features is an important task for efficient video understanding
especially in medical images such as echocardiograms. Convolutional neural networks …

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 life-prolonging therapies …

Accurate estimation of ejection fraction (EF) from echocardiography is of great importance
for evaluation of cardiac function. It is usually obtained by the Simpson's bi-plane method …

Cardiac ultrasound imaging is used to diagnose various heart diseases. Common analysis
pipelines involve manual processing of the video frames by expert clinicians. This suffers …

Abstract Machine learning analysis of biomedical images has seen significant recent
advances. In contrast, there has been much less work on medical videos, despite the fact …

The left ventricular ejection fraction is of significant importance for the early identification and
diagnosis of cardiac disease. However, estimation of the left ventricular ejection fraction with …

Background: We have recently tested an automated machine-learning algorithm that
quantifies left ventricular (LV) ejection fraction (EF) from guidelines-recommended apical …

There is considerable interest in AI systems that can assist a cardiologist to diagnose
echocardiograms, and can also be used to train residents in classifying echocardiograms …

Mitral valve (MV) diseases constitute one of the etiologies of cardiovascular mortality and
morbidity. MV pathologies need evaluating and classifying via echocardiographic videos …

Echocardiography is the commonest medical ultrasound examination, but automated
interpretation is challenging and hinges on correct recognition of the 'view'(imaging plane …