A Light-Weight Deep Video Network: Towards Robust Assessment of Ejection Fraction on Mobile Devices

Echocardiography (echo) is one of the widely used imaging techniques to evaluate cardiac function. Left ventricular ejection fraction (EF) is a commonly assessed echocardiographic measurement to study systolic function and is a primary index of cardiac contractility. EF indicates the percentage of blood volume ejected from the left ventricle in a cardiac cycle. Several deep learning (DL) works have contributed to the automatic measurements of EF in echo via LV segmentation and visual assessment,(1-8) but still the design of a lightweight and robust video-based model for EF estimation in portable mobile environments remains a challenge. To overcome this limitation, here we propose a modified Tiny Video Network (TVN) with sampling-free uncertainty estimation for video-based EF measurement in echo. Our key contribution is to achieve comparable accuracy with the contemporary state-of-the-art video-based model, Echonet-Dynamic approach1 while having a small model size. Moreover, we consider the aleatoric uncertainty in our network to model the inherent noise and ambiguity of EF labels in echo data to improve prediction robustness. The proposed network is suitable for real-time video-based EF estimation compatible with portable mobile devices. For experiments, we use the publically available Echonet-Dynamic dataset1 with 10,030 four-chamber echo videos and their corresponding EF labels. The experiments show the advantages of the proposed method in performance and robustness.