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Measurement of retinal nerve fiber layer thickness with a deep learning algorithm in ischemic optic neuropathy and optic neuritis

Razaghi, Ghazale; Hedayati, Ehsan; Hejazi, Marjaneh; Kafieh, Rahele; Samadi, Melika; Ritch, Robert; Subramanian, Prem S.; Aghsaei Fard, Masoud

Measurement of retinal nerve fiber layer thickness with a deep learning algorithm in ischemic optic neuropathy and optic neuritis Thumbnail


Authors

Ghazale Razaghi

Ehsan Hedayati

Marjaneh Hejazi

Melika Samadi

Robert Ritch

Prem S. Subramanian

Masoud Aghsaei Fard



Abstract

This work aims at determining the ability of a deep learning (DL) algorithm to measure retinal nerve fiber layer (RNFL) thickness from optical coherence tomography (OCT) scans in anterior ischemic optic neuropathy (NAION) and demyelinating optic neuritis (ON). The training/validation dataset included 750 RNFL OCT B-scans. Performance of our algorithm was evaluated on 194 OCT B-scans from 70 healthy eyes, 82 scans from 28 NAION eyes, and 84 scans of 29 ON eyes. Results were compared to manual segmentation as a ground-truth and to RNFL calculations from the built-in instrument software. The Dice coefficient for the test images was 0.87. The mean average RNFL thickness using our U-Net was not different from the manually segmented best estimate and OCT machine data in control and ON eyes. In NAION eyes, while the mean average RNFL thickness using our U-Net algorithm was not different from the manual segmented value, the OCT machine data were different from the manual segmented values. In NAION eyes, the MAE of the average RNFL thickness was 1.18 ± 0.69 μm and 6.65 ± 5.37 μm in the U-Net algorithm segmentation and the conventional OCT machine data, respectively (P = 0.0001).

Citation

Razaghi, G., Hedayati, E., Hejazi, M., Kafieh, R., Samadi, M., Ritch, R., …Aghsaei Fard, M. (2022). Measurement of retinal nerve fiber layer thickness with a deep learning algorithm in ischemic optic neuropathy and optic neuritis. Scientific Reports, 12(1), Article 17109. https://doi.org/10.1038/s41598-022-22135-x

Journal Article Type Article
Acceptance Date Oct 10, 2022
Online Publication Date Oct 12, 2022
Publication Date 2022
Deposit Date Oct 25, 2022
Publicly Available Date Oct 25, 2022
Journal Scientific Reports
Publisher Nature Research
Peer Reviewed Peer Reviewed
Volume 12
Issue 1
Article Number 17109
DOI https://doi.org/10.1038/s41598-022-22135-x

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Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/

Copyright Statement
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/.




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