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Automated Detection of the Stromal Demarcation Line Using Optical Coherence Tomography in Keratoconus Eyes After Corneal Cross-linking

American Journal of Ophthalmology, ISSN: 0002-9394, Vol: 199, Page: 177-183
2019
  • 7
    Citations
  • 0
    Usage
  • 15
    Captures
  • 0
    Mentions
  • 10
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    7
    • Citation Indexes
      7
  • Captures
    15
  • Social Media
    10
    • Shares, Likes & Comments
      10
      • Facebook
        10

Article Description

To evaluate the role of a novel automated detection software as compared to human operators in assessing the presence and depth of stromal demarcation line on optical coherence tomography (OCT) in keratoconus eyes post cross-linking. Reliability analysis study. Two independent operators and an automated detection software examined corneal OCTs of 25 eyes of 25 patients post corneal cross-linking using the Dresden protocol, at 3 months postoperatively. Operators evaluated the presence of the demarcation line and measured its depth by looking at OCT images (128 cuts) on 2 separate occasions 1 week apart. The automated software examined all 128 cuts of each OCT measurement. The mean corneal demarcation line depth was 321.54 ± 47.71, 322.86 ± 45.77, and 309.21 ± 40.98 μm, as computed by the automated detection software and the human operators, respectively. The intraclass correlation coefficients (ICC) between the automated detection software and Observers #1 and #2 were 0.884 and 0.847, respectively ( P <.001). The ICC for interoperator reproducibility was 0.890, and for intraoperator repeatability for Operator #1 and Observer #2 were 0.922 and 0.925, respectively. The ICC for intersoftware repeatability was 1. Bland-Altman plots showed a good agreement between both observers and the software, with adequate 95% limits of agreement. Detection of the demarcation line by human operators is repeatable and reproducible, but it can be further optimized and standardized by an ultrafast and accurate automated software detection tool, providing a reliable indicator for treatment success.

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