September 2016
Volume 57, Issue 12
Open Access
ARVO Annual Meeting Abstract  |   September 2016
A New Method for Measuring Corneal Epithelial Thickness
Author Affiliations & Notes
  • Esther Young
    R&D, Carl Zeiss Meditec, Inc., Dublin, California, United States
  • Homayoun Bagherinia
    R&D, Carl Zeiss Meditec, Inc., Dublin, California, United States
  • Patricia Sha
    R&D, Carl Zeiss Meditec, Inc., Dublin, California, United States
  • Mary K Durbin
    R&D, Carl Zeiss Meditec, Inc., Dublin, California, United States
  • Footnotes
    Commercial Relationships   Esther Young, Carl Zeiss Meditec, Inc. (E); Homayoun Bagherinia, Carl Zeiss Meditec, Inc. (E); Patricia Sha, Carl Zeiss Meditec, Inc. (C); Mary Durbin, Carl Zeiss Meditec, Inc. (E)
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science September 2016, Vol.57, 3377. doi:
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    • Get Citation

      Esther Young, Homayoun Bagherinia, Patricia Sha, Mary K Durbin; A New Method for Measuring Corneal Epithelial Thickness. Invest. Ophthalmol. Vis. Sci. 2016;57(12):3377.

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

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Abstract

Purpose : Evaluating the thickness of the epithelial layer of the cornea can aid in detection of keratoconus (KCN). We reviewed a prospective, multi-site study to develop a new method for measuring corneal epithelial thickness in normal eyes and eyes with keratoconus imaged using CIRRUS OCT.

Methods : The data used in this study was retrospectively reviewed from a prospective multi-site verification study. 16 eyes without ocular pathology ("normal") and 16 eyes with keratoconus were imaged with the Pachymetry scan three times on each of six CIRRUS OCT devices (ZEISS, Dublin, CA). The algorithm identifies the anterior corneal surface and the upper boundary of the Bowman’s layer in each 2-D B-scan (24 radial B-scans over 6 mm) using a combined graph theory and dynamic programming framework. The epithelial thickness map is created as the closest distance from each anterior surface point to the Bowman’s layer followed by interpolation to a complete 2-D map. All images were qualified by manual review to ensure the algorithm properly marked the boundaries of the epithelium. Summary parameters were created by calculating the mean, minimum, and maximum in each hemisphere divided by the vertex. Analysis of variance was used to identify the reproducibility standard deviation and the coefficient of reproducibility was calculated by dividing this by the mean.

Results : Examples of epithelial thickness maps from three instruments for one normal eye and two keratoconic eyes are shown in Figure 1. Due to the importance of data quality for this algorithm and the severe pathology present in some eyes, 20-30% of images were excluded for poor quality or algorithm failures. Three keratoconic eyes were excluded due to variability that occurred from surgical procedures (i.e. keratoplasty, Intacs). Three normal eyes had algorithm failures that prevented statistical analysis, and were excluded as well. Of the rest, the mean and standard deviation for each metric is shown in Table 1.

Conclusions : High-resolution SD-OCT is able to map the epithelial thickness with excellent reproducibility. The epithelial thickness pattern may be useful in distinguishing normal corneas from keratoconic corneas.

This is an abstract that was submitted for the 2016 ARVO Annual Meeting, held in Seattle, Wash., May 1-5, 2016.

 

Figure 1: Epithelial thickness maps and B-Scan for three subjects (Normal Eye NC112, Early Keratoconus CP131, Severe Keratoconus CP140)

Figure 1: Epithelial thickness maps and B-Scan for three subjects (Normal Eye NC112, Early Keratoconus CP131, Severe Keratoconus CP140)

 

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