@article{JMAI5205,
author = {James P. Howard and Jeremy Tan and Matthew J. Shun-Shin and Dina Mahdi and Alexandra N. Nowbar and Ahran D. Arnold and Yousif Ahmad and Peter McCartney and Massoud Zolgharni and Nick W. F. Linton and Nilesh Sutaria and Bushra Rana and Jamil Mayet and Daniel Rueckert and Graham D. Cole and Darrel P. Francis},
title = {Improving ultrasound video classification: an evaluation of novel deep learning methods in echocardiography},
journal = {Journal of Medical Artificial Intelligence},
volume = {3},
number = {0},
year = {2019},
keywords = {},
abstract = {Echocardiography is the commonest medical ultrasound examination, but automated interpretation is challenging and hinges on correct recognition of the ‘view’ (imaging plane and orientation). Current state-of-the-art methods for identifying the view computationally involve 2-dimensional convolutional neural networks (CNNs), but these merely classify individual frames of a video in isolation, and ignore information describing the movement of structures throughout the cardiac cycle. Here we explore the efficacy of novel CNN architectures, including time-distributed networks and two-stream networks, which are inspired by advances in human action recognition. We demonstrate that these new architectures more than halve the error rate of traditional CNNs from 8.1% to 3.9%. These advances in accuracy may be due to these networks’ ability to track the movement of specific structures such as heart valves throughout the cardiac cycle. Finally, we show the accuracies of these new state-of-the-art networks are approaching expert agreement (3.6% discordance), with a similar pattern of discordance between views.},
issn = {2617-2496}, url = {https://jmai.amegroups.org/article/view/5205}
}