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Automated anterior chamber angle pigmentation analyses using 360° gonioscopy.

Masato Matsuo1, Simone Pajaro2, Andrea De Giusti2

  • 1Ophthalmology, Shimane University Faculty of Medicine, Izumo, Japan mmpeaceful@yahoo.ne.jp.

The British Journal of Ophthalmology
|September 1, 2019
PubMed
Summary

An automated algorithm effectively assesses trabecular meshwork pigmentation distribution in the iridocorneal angle. Pigmentation was significantly higher in the inferior sector and quadrant compared to other areas.

Keywords:
angleanterior chamberdiagnostic tests/investigationglaucomaimaging

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Computational Biology

Background:

  • Accurate assessment of iridocorneal angle pigmentation is crucial for diagnosing and monitoring various ocular conditions.
  • Traditional manual grading methods can be subjective and time-consuming.

Purpose of the Study:

  • To evaluate an established algorithm for assessing pigmentation distribution in the iridocorneal angle using gonioscopy images.
  • To compare automated grading with manual grading of trabecular meshwork pigmentation.

Main Methods:

  • Automated and manual Scheie's pigmentation grading of trabecular meshwork in 75 open-angle eyes.
  • Images were obtained via automated gonioscopy.
  • Statistical comparison of pigmentation density between automated and manual techniques across sectors and quadrants.

Main Results:

  • Substantial agreement (kappa=0.70) was found between automated and manual grading methods.
  • Automated grading showed significantly higher pigmentation in the inferior sector and quadrant compared to other areas (p<0.05).
  • Similar findings were observed for manual grading, confirming the inferior sector's higher pigmentation.

Conclusions:

  • The developed algorithm successfully classifies anterior chamber angle pigmentation distribution.
  • Automated grading demonstrates good agreement with manual grading.
  • The inferior sector and quadrant consistently show higher pigmentation, a finding supported by both automated and manual assessments.