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Measurement Reliability for Keratitis Morphology.

Matthias F Kriegel1,2, Jessica Loo3, Sina Farsiu3,4

  • 1Department of Ophthalmology and Visual Sciences, W.K. Kellogg Eye Center, University of Michigan, Ann Arbor, MI.

Cornea
|August 25, 2020
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Summary

Manual annotation of microbial keratitis (MK) features on slit-lamp photography (SLP) shows good reliability for quantifying anatomical and morphological characteristics. Optimization of annotation protocols is recommended to minimize inherent variability in manual assessments.

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Computational Biology

Background:

  • Microbial keratitis (MK) is a serious eye infection requiring accurate assessment of anatomical and morphological features.
  • Slit-lamp photography (SLP) is a key diagnostic tool, but manual annotation of its images can be subjective.

Purpose of the Study:

  • To evaluate the reliability of manual annotation for quantifying corneal anatomical and MK morphological features on SLP images.
  • To assess inter- and intra-grader variability in feature annotation.

Main Methods:

  • Prospective enrollment of 75 MK patients with epithelial defects.
  • Independent manual annotation of features (corneal limbus, pupil, epithelial defect, stromal infiltrate, white blood cell infiltration, hypopyon) on SLP images by two physicians.
  • Reliability assessment using intraclass correlation coefficients (ICCs) and dice similarity coefficients (DSCs).

Main Results:

  • Good to fair annotation overlap (DSCs ranging from 0.80 to 0.97) and high reliability (ICCs ranging from 0.67 to 1.00) were observed between graders and for repeat annotations.
  • Recalculation for stromal infiltrate area, with agreement on white blood cell infiltration presence/absence, improved inter-grader ICC to 0.91 and DSC to 0.86.

Conclusions:

  • Manual annotation is useful for quantifying MK features on SLP images.
  • Inherent variability necessitates optimization of annotation protocols.
  • Future directions include using multiple annotators or automated annotation software.