| Field |
Value |
Language |
|
dc.contributor.author |
Panahi, A |
|
|
dc.contributor.author |
Rezaee, A |
|
|
dc.contributor.author |
Hajati, F |
|
|
dc.contributor.author |
Shariflou, S |
|
|
dc.contributor.author |
Agar, A |
|
|
dc.contributor.author |
Golzan, SM |
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|
dc.date.accessioned |
2023-09-06T05:32:01Z |
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|
dc.date.available |
2023-09-06T05:32:01Z |
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|
dc.identifier.citation |
Scientific Reports, 13, (1) |
|
|
dc.identifier.issn |
2045-2322 |
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|
dc.identifier.uri |
http://hdl.handle.net/10453/171962
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|
dc.description.abstract |
<jats:title>Abstract</jats:title><jats:p>The presence or absence of spontaneous retinal venous pulsations (SVP) provides clinically significant insight into the hemodynamic status of the optic nerve head. Reduced SVP amplitudes have been linked to increased intracranial pressure and glaucoma progression. Currently, monitoring for the presence or absence of SVPs is performed subjectively and is highly dependent on trained clinicians. In this study, we developed a novel end-to-end deep model, called U3D-Net, to objectively classify SVPs as present or absent based on retinal fundus videos. The U3D-Net architecture consists of two distinct modules: an optic disc localizer and a classifier. First, a fast attention recurrent residual U-Net model is applied as the optic disc localizer. Then, the localized optic discs are passed on to a deep convolutional network for SVP classification. We trained and tested various time-series classifiers including 3D Inception, 3D Dense-ResNet, 3D ResNet, Long-term Recurrent Convolutional Network, and ConvLSTM. The optic disc localizer achieved a dice score of 95% for locating the optic disc in 30 milliseconds. Amongst the different tested models, the 3D Inception model achieved an accuracy, sensitivity, and F1-Score of 84 ± 5%, 90 ± 8%, and 81 ± 6% respectively, outperforming the other tested models in classifying SVPs. To the best of our knowledge, this research is the first study that utilizes a deep neural network for an autonomous and objective classification of SVPs using retinal fundus videos.</jats:p> |
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dc.language |
en |
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dc.publisher |
Springer Science and Business Media LLC |
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dc.relation |
Google LLC |
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dc.relation.ispartof |
Scientific Reports |
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|
dc.relation.isbasedon |
10.1038/s41598-023-41110-8 |
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dc.rights |
info:eu-repo/semantics/restrictedAccess |
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dc.rights.uri |
Open Access This article is licensed under a Creative Commons Attribution 4.0 International
License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creativecommons. org/ licenses/ by/4. 0/.
© The Author(s) 2023 |
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|
dc.title |
Autonomous assessment of spontaneous retinal venous pulsations in fundus videos using a deep learning framework |
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dc.type |
Journal Article |
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|
utslib.citation.volume |
13 |
|
|
pubs.organisational-group |
/University of Technology Sydney |
|
|
pubs.organisational-group |
/University of Technology Sydney/Faculty of Health |
|
|
pubs.organisational-group |
/University of Technology Sydney/Strength - CHT - Health Technologies |
|
|
pubs.organisational-group |
/University of Technology Sydney/Centre for Health Technologies (CHT) |
|
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utslib.copyright.status |
open_access |
* |
|
dc.date.updated |
2023-09-06T05:31:59Z |
|
|
pubs.issue |
1 |
|
|
pubs.publication-status |
Published online |
|
|
pubs.volume |
13 |
|
|
utslib.citation.issue |
1 |
|
