Echocardiographic Phase Detection Using Neural Networks

Elisabeth Sarah Lane, Neda Azarmehr, Jevgeni Jevsikov, James P Howard, Matthew Shun-shin, Darrel P Francis, Massoud Zolgharni

Keywords: Echocardiography, Cardiac imaging, Deep learning, Phase detection.

TL;DR: Multibeat echocardiographic phase detection using neural networks for videos of arbitrary length.

Abstract: Accurate identification of end-diastolic and end-systolic frames in echocardiographic cine loops is essential when measuring cardiac function. Manual selection by human experts is challenging and error prone. This paper presents a deep neural network trained and tested on multi-centre patient data for accurate phase detection in apical four-chamber videos of arbitrary length, spanning several heartbeats, with performance indistinguishable from that of human experts.

Paper Type: both

Primary Subject Area: Detection and Diagnosis

Secondary Subject Area: Transfer Learning and Domain Adaptation

Paper Status: based on accepted/submitted journal paper

Source Code Url: https://github.com/intsav/EchoPhaseDetection

Data Set Url:

https://intsav.github.io/
https://echonet.github.io/dynamic/

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