June 2020
Volume 61, Issue 7
Free
ARVO Annual Meeting Abstract  |   June 2020
Classification of proliferative diabetic retinopathy and non-diabetic subjects by an ordinary least squares modeling method applied to OCTA images
Author Affiliations & Notes
  • Jennifer Cano
    Department of Ophthalmology, University of Southern California, Los Angeles, California, United States
  • William D O'neill
    Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, United States
  • Richard D Penn
    Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, United States
    Department of Neurosurgery, Rush University and Hospital, Chicago, Illinois, United States
  • Norman P Blair
    Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, Illinois, United States
  • Mahnaz Shahidi
    Department of Ophthalmology, University of Southern California, Los Angeles, California, United States
  • Footnotes
    Commercial Relationships   Jennifer Cano, None; William O'neill, None; Richard Penn, None; Norman Blair, None; Mahnaz Shahidi, None
  • Footnotes
    Support  NIH grants DK104393 and EY029220, and Research to Prevent Blindness Foundation
Investigative Ophthalmology & Visual Science June 2020, Vol.61, 1613. doi:
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Jennifer Cano, William D O'neill, Richard D Penn, Norman P Blair, Mahnaz Shahidi; Classification of proliferative diabetic retinopathy and non-diabetic subjects by an ordinary least squares modeling method applied to OCTA images. Invest. Ophthalmol. Vis. Sci. 2020;61(7):1613.

      Download citation file:


      © ARVO (1962-2015); The Authors (2016-present)

      ×
  • Supplements
Abstract

Purpose : As the rates for development of diabetic retinopathy (DR) continue to rise, innovative computational methods have been developed to improve screening. The purpose of the study was to report an ordinary least squares (OLS) modeling method applied to optical coherence tomography angiography (OCTA) images for classifying proliferative DR (PDR) and non-diabetic control (NC) subjects.

Methods : Twenty-two NC and 13 PDR subjects participated in the study. OCTA images of the superficial capillary plexus were obtained in a 6 x 6 mm macular region centered on the fovea. Each image was considered a solution to a linear autoregressive equation and 25 coefficients were estimated from the OLS model. Vessel metrics, namely vessel tortuosity index (VTI), vessel density (VD), spacing between small vessels (SSV), and between large vessels (SLV) were quantified from OCTA images using established methods. Vessel metrics were compared between NC and PDR groups and correlated with OLS coefficients. Logistic regressions were performed to identify individual and multiple coefficients associated with groups. Performance, predictive ability, and validity of the multivariate logistic regression were evaluated.

Results : Age and sex did not differ between groups (P ≥ 0.09). VD and SSV were decreased (P < 0.0001) and SLV was increased (P < 0.0001) in PDR compared to NC group. VTI did not differ between groups (P = 0.34). VTI and VD were each correlated with 4 coefficients (P ≤ 0.04; N=35). SSV and SLV were correlated with 3 and 5 coefficients, respectively (P ≤ 0.04; N=35). Six coefficients that were individually associated with groups showed improved performance in multivariate logistic regression (Likelihood Ratio X2 = 38.17, P < 0.0001) and correctly classified 94% of subjects. The area under the receiver operating characteristic curve (AUROC) was 0.99 and AUROCs with and without cross validation did not differ significantly (P = 0.07), thereby validating the model.

Conclusions : Using OCTA images, an OLS modeling method generated coefficients that correlated with vessel metrics and were utilized to derive predicted probabilities of PDR and NC. With further development, the method may have potential clinical utility and contribute to image-based computer-aided screening and classification of stages of DR and other ocular and systemic diseases.

This is a 2020 ARVO Annual Meeting abstract.

×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×