June 2021
Volume 62, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2021
Detection of Corneal Ectasia Using OCT Maps of Pachymetry and Posterior Surface Mean Curvature
Author Affiliations & Notes
  • Elias Pavlatos
    Oregon Health & Science University, Portland, Oregon, United States
  • David Huang
    Oregon Health & Science University, Portland, Oregon, United States
  • Yan Li
    Oregon Health & Science University, Portland, Oregon, United States
  • Footnotes
    Commercial Relationships   Elias Pavlatos, Optovue, Inc (F); David Huang, Optovue, Inc. (F), Optovue, Inc. (I), Optovue, Inc. (P), Optovue, Inc. (R); Yan Li, Optovue, Inc. (F), Optovue, Inc. (P)
  • Footnotes
    Support  National Institutes of Health grants R01EY028755, R01EY029023, T32EY023211, P30EY010572; a research grant and equipment support from Optovue, Inc., Fremont, CA; unrestricted grants to Casey Eye Institute from Research to Prevent Blindness, Inc., New York, NY.
Investigative Ophthalmology & Visual Science June 2021, Vol.62, 2038. doi:
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    • Get Citation

      Elias Pavlatos, David Huang, Yan Li; Detection of Corneal Ectasia Using OCT Maps of Pachymetry and Posterior Surface Mean Curvature. Invest. Ophthalmol. Vis. Sci. 2021;62(8):2038.

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

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Abstract

Purpose : To formulate an Ectasia index to quantify the abnormal corneal thinning and posterior surface steepening that is associated with ectatic pathologies such as keratoconus.

Methods : 32 eyes from normal subjects and 131 eyes with varying degrees of keratoconus severity were imaged using a Fourier-domain OCT system (Avanti, Optovue, Fremont, CA). Maps of corneal pachymetry and mean curvature of the posterior corneal surface were generated. All pachymetry and mean curvature maps were converted to pattern deviation (PD) maps to show differences in normalized thickness and curvature relative to the healthy population (Li et al, JCRS, 2016). The minimum value on the pachymetry PD map was located, and a 2D symmetric Gaussian function was fit to a sub-region of the map centered at this location. The average vector between the pachymetry PD map minimum and the mean curvature PD map maximum was calculated using all of the keratoconus eyes. Gaussian fitting of the mean curvature PD map was performed with the center of the fit shifted by this average vector with respect to the location of the pachymetry PD minimum. The Ectasia index was calculated from the product of the two Gaussian fits. K-fold cross-validation was used to compare the diagnostic performance of the Ectasia index compared to minimum pachymetry and maximum mean curvature.

Results : The ectatic pattern of coincident corneal thinning and posterior surface steeping was evident for the keratoconus eyes, but not for the normal eyes (Figure 1). The average Ectasia index value for the normal eyes was -0.1 ± 1.0%. The Ectasia index was larger for the manifest, subclinical, and forme fruste keratoconus groups, with average values of -19.4 ± 6.9%, -13.7 ± 4.6%, and -3.7 ± 3.9% respectively (all p < 0.0002). The Ectasia index was statistically more sensitive than minimum pachymetry in detecting manifest keratoconus and more sensitive than maximum mean curvature in detecting forme fruste keratoconus. The specificity of the Ectasia index did not differ statistically from the specificity of the two other metrics.

Conclusions : The Ectasia index was effective in differentiating between normal and keratoconic eyes and could therefore be a useful additional metric for clinicians to consider when screening for ectasia.

This is a 2021 ARVO Annual Meeting abstract.

 

Fig 1. Representative pattern deviation and Ectasia maps for an eye from each group

Fig 1. Representative pattern deviation and Ectasia maps for an eye from each group

 

Fig 2. Summary of repeated 5-fold cross-validation results

Fig 2. Summary of repeated 5-fold cross-validation results

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