Lane, E.S., Azarmehr, N. orcid.org/0000-0002-6367-207X, Jevsikov, J. et al. (5 more authors) (2021) Multibeat echocardiographic phase detection using deep neural networks. Computers in Biology and Medicine, 133. 104373. ISSN 0010-4825
Abstract
Background
Accurate identification of end-diastolic and end-systolic frames in echocardiographic cine loops is important, yet challenging, for human experts. Manual frame selection is subject to uncertainty, affecting crucial clinical measurements, such as myocardial strain. Therefore, the ability to automatically detect frames of interest is highly desirable.
Methods
We have developed deep neural networks, trained and tested on multi-centre patient data, for the accurate identification of end-diastolic and end-systolic frames in apical four-chamber 2D multibeat cine loop recordings of arbitrary length. Seven experienced cardiologist experts independently labelled the frames of interest, thereby providing infallible annotations, allowing for observer variability measurements.
Results
When compared with the ground-truth, our model shows an average frame difference of −0.09 ± 1.10 and 0.11 ± 1.29 frames for end-diastolic and end-systolic frames, respectively. When applied to patient datasets from a different clinical site, to which the model was blind during its development, average frame differences of −1.34 ± 3.27 and −0.31 ± 3.37 frames were obtained for both frames of interest. All detection errors fall within the range of inter-observer variability: [-0.87, −5.51]±[2.29, 4.26] and [-0.97, −3.46]±[3.67, 4.68] for ED and ES events, respectively.
Conclusions
The proposed automated model can identify multiple end-systolic and end-diastolic frames in echocardiographic videos of arbitrary length with performance indistinguishable from that of human experts, but with significantly shorter processing time.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 Published by Elsevier Ltd. This is an author produced version of a paper subsequently published in Computers in Biology and Medicine. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | Echocardiography; Cardiac imaging; Deep learning; Phase detection |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Clinical Dentistry (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 17 Aug 2021 14:34 |
Last Modified: | 06 Apr 2022 00:38 |
Status: | Published |
Publisher: | Elsevier BV |
Refereed: | Yes |
Identification Number: | 10.1016/j.compbiomed.2021.104373 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:177142 |
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Filename: MultibeatEchocardiographicPhaseDetection-clean__1_.pdf
Licence: CC-BY-NC-ND 4.0