Density-based classification in diabetic retinopathy through thickness of retinal layers from optical coherence tomography

Abstract Diabetic retinopathy (DR) is a severe retinal disorder that can lead to vision loss, however, its underlying mechanism has not been fully understood. Previous studies have taken advantage of Optical Coherence Tomography (OCT) and shown that the thickness of individual retinal layers are aff...

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Autores principales:	Shariq Mohammed, Tingyang Li, Xing D. Chen, Elisa Warner, Anand Shankar, Maria Fernanda Abalem, Thiran Jayasundera, Thomas W. Gardner, Arvind Rao
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4639e1fdf79f428792cee301ef41c7e1
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spelling	oai:doaj.org-article:4639e1fdf79f428792cee301ef41c7e12021-12-02T18:51:07ZDensity-based classification in diabetic retinopathy through thickness of retinal layers from optical coherence tomography10.1038/s41598-020-72813-x2045-2322https://doaj.org/article/4639e1fdf79f428792cee301ef41c7e12020-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-72813-xhttps://doaj.org/toc/2045-2322Abstract Diabetic retinopathy (DR) is a severe retinal disorder that can lead to vision loss, however, its underlying mechanism has not been fully understood. Previous studies have taken advantage of Optical Coherence Tomography (OCT) and shown that the thickness of individual retinal layers are affected in patients with DR. However, most studies analyzed the thickness by calculating summary statistics from retinal thickness maps of the macula region. This study aims to apply a density function-based statistical framework to the thickness data obtained through OCT, and to compare the predictive power of various retinal layers to assess the severity of DR. We used a prototype data set of 107 subjects which are comprised of 38 non-proliferative DR (NPDR), 28 without DR (NoDR), and 41 controls. Based on the thickness profiles, we constructed novel features which capture the variation in the distribution of the pixel-wise retinal layer thicknesses from OCT. We quantified the predictive power of each of the retinal layers to distinguish between all three pairwise comparisons of the severity in DR (NoDR vs NPDR, controls vs NPDR, and controls vs NoDR). When applied to this preliminary DR data set, our density-based method demonstrated better predictive results compared with simple summary statistics. Furthermore, our results indicate considerable differences in retinal layer structuring based on the severity of DR. We found that: (a) the outer plexiform layer is the most discriminative layer for classifying NoDR vs NPDR; (b) the outer plexiform, inner nuclear and ganglion cell layers are the strongest biomarkers for discriminating controls from NPDR; and (c) the inner nuclear layer distinguishes best between controls and NoDR.Shariq MohammedTingyang LiXing D. ChenElisa WarnerAnand ShankarMaria Fernanda AbalemThiran JayasunderaThomas W. GardnerArvind RaoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-13 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Shariq Mohammed Tingyang Li Xing D. Chen Elisa Warner Anand Shankar Maria Fernanda Abalem Thiran Jayasundera Thomas W. Gardner Arvind Rao Density-based classification in diabetic retinopathy through thickness of retinal layers from optical coherence tomography
description	Abstract Diabetic retinopathy (DR) is a severe retinal disorder that can lead to vision loss, however, its underlying mechanism has not been fully understood. Previous studies have taken advantage of Optical Coherence Tomography (OCT) and shown that the thickness of individual retinal layers are affected in patients with DR. However, most studies analyzed the thickness by calculating summary statistics from retinal thickness maps of the macula region. This study aims to apply a density function-based statistical framework to the thickness data obtained through OCT, and to compare the predictive power of various retinal layers to assess the severity of DR. We used a prototype data set of 107 subjects which are comprised of 38 non-proliferative DR (NPDR), 28 without DR (NoDR), and 41 controls. Based on the thickness profiles, we constructed novel features which capture the variation in the distribution of the pixel-wise retinal layer thicknesses from OCT. We quantified the predictive power of each of the retinal layers to distinguish between all three pairwise comparisons of the severity in DR (NoDR vs NPDR, controls vs NPDR, and controls vs NoDR). When applied to this preliminary DR data set, our density-based method demonstrated better predictive results compared with simple summary statistics. Furthermore, our results indicate considerable differences in retinal layer structuring based on the severity of DR. We found that: (a) the outer plexiform layer is the most discriminative layer for classifying NoDR vs NPDR; (b) the outer plexiform, inner nuclear and ganglion cell layers are the strongest biomarkers for discriminating controls from NPDR; and (c) the inner nuclear layer distinguishes best between controls and NoDR.
format	article
author	Shariq Mohammed Tingyang Li Xing D. Chen Elisa Warner Anand Shankar Maria Fernanda Abalem Thiran Jayasundera Thomas W. Gardner Arvind Rao
author_facet	Shariq Mohammed Tingyang Li Xing D. Chen Elisa Warner Anand Shankar Maria Fernanda Abalem Thiran Jayasundera Thomas W. Gardner Arvind Rao
author_sort	Shariq Mohammed
title	Density-based classification in diabetic retinopathy through thickness of retinal layers from optical coherence tomography
title_short	Density-based classification in diabetic retinopathy through thickness of retinal layers from optical coherence tomography
title_full	Density-based classification in diabetic retinopathy through thickness of retinal layers from optical coherence tomography
title_fullStr	Density-based classification in diabetic retinopathy through thickness of retinal layers from optical coherence tomography
title_full_unstemmed	Density-based classification in diabetic retinopathy through thickness of retinal layers from optical coherence tomography
title_sort	density-based classification in diabetic retinopathy through thickness of retinal layers from optical coherence tomography
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/4639e1fdf79f428792cee301ef41c7e1
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Density-based classification in diabetic retinopathy through thickness of retinal layers from optical coherence tomography

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