An automated approach for early detection of diabetic retinopathy using SD-OCT images

Ahmed H. ElTanboly; Agustina Palacio; Ahmed M. Shalaby; Andrew E. Switala; Omar Helmy; Shlomit Schaal; Ayman El-Baz

doi:10.2741/e817

An automated approach for early detection of diabetic retinopathy using SD-OCT images

Ahmed H. ElTanboly, Agustina Palacio, Ahmed M. Shalaby, Andrew E. Switala, Omar Helmy, Shlomit Schaal, Ayman El-Baz

Houston Methodist

Research output: Contribution to journal › Article › peer-review

18 Scopus citations

Abstract

This study was to demonstrate the feasibility of an automatic approach for early detection of diabetic retinopathy (DR) from SD-OCT images. These scans were prospectively collected from 200 subjects through the fovea then were automatically segmented, into 12 layers. Each layer was characterized by its thickness, tortuosity, and normalized reflectivity. 26 diabetic patients, without DR changes visible by funduscopic examination, were matched with 26 controls, according to age and sex, for purposes of statistical analysis using mixed effects ANOVA. The INL was narrower in diabetes (p = 0.14), while the NFL (p = 0.04) and IZ (p = 0.34) were thicker. Tortuosity of layers NFL through the OPL was greater in diabetes (all p < 0.1), while significantly greater normalized reflectivity was observed in the MZ and OPR (both p < 0.01) as well as ELM and IZ (both p < 0.5). A novel automated method enables to provide quantitative analysis of the changes in each layer of the retina that occur with diabetes. In turn, carries the promise to a reliable non-invasive diagnostic tool for early detection of DR.

Original language	English (US)
Pages (from-to)	197-207
Number of pages	11
Journal	Frontiers in Bioscience - Elite
Volume	10
Issue number	2
DOIs	https://doi.org/10.2741/e817
State	Published - Jan 1 2018

Keywords

DR
Diabetic Retinopathy
Reflectivity
Retinal Segmentation
SD-OCT
Spectral Domain Optical Coherence Tomography
Thickness
Tortuosity

ASJC Scopus subject areas

General Biochemistry, Genetics and Molecular Biology
General Immunology and Microbiology

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10.2741/e817

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title = "An automated approach for early detection of diabetic retinopathy using SD-OCT images",

abstract = "This study was to demonstrate the feasibility of an automatic approach for early detection of diabetic retinopathy (DR) from SD-OCT images. These scans were prospectively collected from 200 subjects through the fovea then were automatically segmented, into 12 layers. Each layer was characterized by its thickness, tortuosity, and normalized reflectivity. 26 diabetic patients, without DR changes visible by funduscopic examination, were matched with 26 controls, according to age and sex, for purposes of statistical analysis using mixed effects ANOVA. The INL was narrower in diabetes (p = 0.14), while the NFL (p = 0.04) and IZ (p = 0.34) were thicker. Tortuosity of layers NFL through the OPL was greater in diabetes (all p < 0.1), while significantly greater normalized reflectivity was observed in the MZ and OPR (both p < 0.01) as well as ELM and IZ (both p < 0.5). A novel automated method enables to provide quantitative analysis of the changes in each layer of the retina that occur with diabetes. In turn, carries the promise to a reliable non-invasive diagnostic tool for early detection of DR.",

keywords = "DR, Diabetic Retinopathy, Reflectivity, Retinal Segmentation, SD-OCT, Spectral Domain Optical Coherence Tomography, Thickness, Tortuosity",

author = "ElTanboly, {Ahmed H.} and Agustina Palacio and Shalaby, {Ahmed M.} and Switala, {Andrew E.} and Omar Helmy and Shlomit Schaal and Ayman El-Baz",

note = "Funding Information: Ayman El-Baz and Shlomit Schaal share joint senior authorship of this article. This project was supported in part by the Coulter Translational Partnership Grant (Schaal and El-Baz 2015), by an unrestricted institutional grant from Research to Prevent Blindness (RPB), and by the University of Louisville Summer Research Scholar Program (Schaal and Neyer 2015).This project was supported by Zeiss in the means of Zeiss Cirrus HD-OCT 5000 machine loan to the University of Louisville. Two provisional Funding Information: Ayman El-Baz and Shlomit Schaal share joint senior authorship of this article. This project was supported in part by the Coulter Translational Partnership Grant (Schaal and El-Baz 2015), by an unrestricted institutional grant from Research to Prevent Blindness (RPB), and by the University of Louisville Summer Research Scholar Program (Schaal and Neyer 2015).This project was supported by Zeiss in the means of Zeiss Cirrus HD-OCT 5000 machine loan to the University of Louisville. Two provisional patent applications have been filed regarding this technology: US Provisional Patent # 50290-6 and # 62/256,980 (Schaal and Hajrasouliha 2015). Publisher Copyright: {\textcopyright} 2018 Frontiers in Bioscience. All Rights Reserved.",

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AU - Helmy, Omar

AU - Schaal, Shlomit

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N2 - This study was to demonstrate the feasibility of an automatic approach for early detection of diabetic retinopathy (DR) from SD-OCT images. These scans were prospectively collected from 200 subjects through the fovea then were automatically segmented, into 12 layers. Each layer was characterized by its thickness, tortuosity, and normalized reflectivity. 26 diabetic patients, without DR changes visible by funduscopic examination, were matched with 26 controls, according to age and sex, for purposes of statistical analysis using mixed effects ANOVA. The INL was narrower in diabetes (p = 0.14), while the NFL (p = 0.04) and IZ (p = 0.34) were thicker. Tortuosity of layers NFL through the OPL was greater in diabetes (all p < 0.1), while significantly greater normalized reflectivity was observed in the MZ and OPR (both p < 0.01) as well as ELM and IZ (both p < 0.5). A novel automated method enables to provide quantitative analysis of the changes in each layer of the retina that occur with diabetes. In turn, carries the promise to a reliable non-invasive diagnostic tool for early detection of DR.

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An automated approach for early detection of diabetic retinopathy using SD-OCT images

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Keywords

ASJC Scopus subject areas

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