June 2020
Volume 61, Issue 7
Free
ARVO Annual Meeting Abstract  |   June 2020
Differentiation of Papilledema from Non-Arteritic Anterior Ischemic Optic Neuropathy (NAION) using 3D Retinal Morphological Features of Optical Coherence Tomography
Author Affiliations & Notes
  • Jui-Kai Wang
    Center for the Prevention and Treatment of Visual Loss, Iowa City VA Health System, Iowa City, Iowa, United States
    Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, United States
  • Matthew J. Thurtell
    Department of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City, Iowa, United States
  • Randy Kardon
    Center for the Prevention and Treatment of Visual Loss, Iowa City VA Health System, Iowa City, Iowa, United States
    Department of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City, Iowa, United States
  • Mona K Garvin
    Center for the Prevention and Treatment of Visual Loss, Iowa City VA Health System, Iowa City, Iowa, United States
    Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, United States
  • Footnotes
    Commercial Relationships   Jui-Kai Wang, None; Matthew Thurtell, None; Randy Kardon, Department of Veterans Affairs Research Foundation, Iowa City, IA (S), Fight for Sight Inc (S); Mona Garvin, University of Iowa (P)
  • Footnotes
    Support  I01 RX001786; R01 EY023279
Investigative Ophthalmology & Visual Science June 2020, Vol.61, 3950. doi:
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      Jui-Kai Wang, Matthew J. Thurtell, Randy Kardon, Mona K Garvin; Differentiation of Papilledema from Non-Arteritic Anterior Ischemic Optic Neuropathy (NAION) using 3D Retinal Morphological Features of Optical Coherence Tomography. Invest. Ophthalmol. Vis. Sci. 2020;61(7):3950.

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

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Abstract

Purpose : Variation in peripapillary retinal deformation may help differentiate causes of optic disc swelling. Previously, we presented a machine-learning method for differentiating papilledema from NAION using a mixture of 2D/3D optical coherence tomography (OCT) features (Miller et al., ARVO 2018). In this study, we improved the previous algorithm performance by utilizing a pure set of 3D retinal shape features in a larger dataset.

Methods : For each optic-nerve-head (ONH) OCT volumetric scan, the internal limiting membrane (ILM) and Bruch's membrane (BM) were automatically segmented; then the volume between the ILM and BM (i.e., ONHV) was computed. Next, 23 3D retinal shape models of the ILM, BM, and ILM+BM were generated using principal component analyses (PCA) based on four manual landmarks on the BM opening contour and 481 automated equidistant landmarks on the ILM and BM surfaces. An example is shown in Fig. 1. Random forest classifiers were then applied to interpret these shape features and to classify the papilledema/NAION cases. Leave-one-subject-out cross-validation was used for evaluation.

Results : The dataset included 57 papilledema and 57 NAION subjects who were matched for ONHV from the University of Iowa Hospitals and Clinics. When the random forest classifiers considered all the 23 features from the ILM, BM, and ILM+BM 3D shape models, the overall classification accuracy rate achieved was 86% [i.e., total: 98/114; 89% (82%) for the papilledema (NAION) group]. Fig. 2 shows the feature importance and the shape variations of the top two models. Compared to the classifiers only using the traditional 2D and 3D BM shape features, the accuracy rates dropped to 73% and 77%, respectively.

Conclusions : Our proposed method improves the differentiation of papilledema from NAION eyes using 3D retinal morphological information in regular ONH OCT volumetric scans. This study also sets up a foundation for future efforts to investigate the association between retinal shape change and intracranial biomechanical stress/strain.

This is a 2020 ARVO Annual Meeting abstract.

 

Landmark placement. (A) RPE en-face image with 4 manual landmarks (B) Radial scanning lines in every 15o (C) The OCT central B-scan with automated landmarks (D, E) Projected view of 192 BM [cyan] and 289 ILM [green] landmarks.

Landmark placement. (A) RPE en-face image with 4 manual landmarks (B) Radial scanning lines in every 15o (C) The OCT central B-scan with automated landmarks (D, E) Projected view of 192 BM [cyan] and 289 ILM [green] landmarks.

 

Feature importance and shape variations of the top two models.

Feature importance and shape variations of the top two models.

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