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

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


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 languageEnglish (US)
Pages (from-to)197-207
Number of pages11
JournalFrontiers in Bioscience - Elite
Issue number2
StatePublished - Jan 1 2018


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

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)


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