June 2022
Volume 63, Issue 7
Open Access
ARVO Annual Meeting Abstract  |   June 2022
Focal Loss Analysis of Peripapillary Nerve Fiber Layer Reflectance for Glaucoma Diagnosis
Ou Tan; Dongseok Choi; Aiyin Chen; Brian Francis; Joel S Schuman; Rohit Varma; David S Greenfield; David Huang
Author Affiliations & Notes
  • Ou Tan
    Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, United States
  • Dongseok Choi
    Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, United States
  • Aiyin Chen
    Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, United States
  • Brian Francis
    Doheny Eye Center, University of California Los Angeles David Geffen School of Medicine, Los Angeles, California, United States
  • Joel S Schuman
    NYU Langone Health, New York, New York, United States
  • Rohit Varma
    Southern California Eyecare and Vision research Institute, Los Angeles, California, United States
  • David S Greenfield
    Bascom Palmer Eye Institute, University of Miami School of Medicine, Miami, Florida, United States
  • David Huang
    Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, United States
  • Footnotes
    Commercial Relationships   Ou Tan Optovue, Code P (Patent); Dongseok Choi None; Aiyin Chen None; Brian Francis None; Joel Schuman None; Rohit Varma None; David Greenfield None; David Huang Optovue, Code F (Financial Support), Optovue, Code I (Personal Financial Interest), Optovue, Code O (Owner), Optovue, Code P (Patent)
  • Footnotes
    Support  : NIH grants R21 EY032146, R01 EY023285, and P30 EY010572, and an unrestricted grant from Research to Prevent Blindness to Casey Eye Institute.
Investigative Ophthalmology & Visual Science June 2022, Vol.63, 643 – A0383. doi:
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      Ou Tan, Dongseok Choi, Aiyin Chen, Brian Francis, Joel S Schuman, Rohit Varma, David S Greenfield, David Huang; Focal Loss Analysis of Peripapillary Nerve Fiber Layer Reflectance for Glaucoma Diagnosis. Invest. Ophthalmol. Vis. Sci. 2022;63(7):643 – A0383.

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Abstract

Purpose : To evaluate nerve fiber layer (NFL) reflectance for glaucoma diagnosis using a large dataset.

Methods : Participants were imaged with 4.9mm ONH scans using spectral-domain optical coherence tomography (OCT). The NFL reflectance map was reconstructed from 13 concentric rings of optic nerve head(ONH) scan, then processed by an azimuthal filter to reduce directional reflectance bias due to variation of beam incidence angle. The peripapillary thickness and reflectance maps were both divided into 96 superpixels. Low-reflectance and low-thickness superpixels were defined as values below the 5th percentile normative reference for that location. Focal reflectance loss was measured by summing loss, relative to the normal reference average, in low-reflectance superpixels. Focal thickness loss was calculated in a similar fashion. The area under receiving characteristic curve (AROC) was used to assess diagnostic accuracy.

Results : Fifty-three normal, 196 pre-perimetric, 132 early perimetric, and 59 moderate and advanced perimetric glaucoma participants were included from the Advanced Imaging for Glaucoma Study. Sixty-seven percent of glaucomatous reflectance maps showed characteristic contiguous wedge or diffuse defects. Focal NFL reflectance loss had significantly higher diagnostic accuracy than the best NFL thickness parameters (both map-based and profile-based): AROC 0.80 v. 0.75 (p<0.004) for distinguishing glaucoma eyes from healthy control eyes. The diagnostic sensitivity was also significantly higher at both 99% specificity operating points.

Conclusions : Focal NFL reflectance loss improved glaucoma diagnostic accuracy compared to the standard NFL thickness parameters.

This abstract was presented at the 2022 ARVO Annual Meeting, held in Denver, CO, May 1-4, 2022, and virtually.

 

Table 1. Diagnostic Accuracy of Nerve Fiber Layer Thickness and Reflectance for Glaucoma. AROC: The area under receiving characteristic curve, value are estimated AROC ± standard error. *: p-value < 0.05 compared to the thickness profile average. +: p-value < 0.05 compare to best NFL thickness parameter
View OriginalDownload Slide

Table 1. Diagnostic Accuracy of Nerve Fiber Layer Thickness and Reflectance for Glaucoma. AROC: The area under receiving characteristic curve, value are estimated AROC ± standard error. *: p-value < 0.05 compared to the thickness profile average. +: p-value < 0.05 compare to best NFL thickness parameter

Table 1. Diagnostic Accuracy of Nerve Fiber Layer Thickness and Reflectance for Glaucoma. AROC: The area under receiving characteristic curve, value are estimated AROC ± standard error. *: p-value < 0.05 compared to the thickness profile average. +: p-value < 0.05 compare to best NFL thickness parameter

Table 1. Diagnostic Accuracy of Nerve Fiber Layer Thickness and Reflectance for Glaucoma. AROC: The area under receiving characteristic curve, value are estimated AROC ± standard error. *: p-value < 0.05 compared to the thickness profile average. +: p-value < 0.05 compare to best NFL thickness parameter
View OriginalDownload Slide

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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Copyright © 2025 Association for Research in Vision and Ophthalmology.
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