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An Artificial Intelligence Approach to Detect Visual Field Progression in Glaucoma Based on Spatial Pattern Analysis.

Mengyu Wang1, Lucy Q Shen2, Louis R Pasquale2,3

  • 1Schepens Eye Research Institute, Harvard Medical School, Boston, Massachusetts, United States.

Investigative Ophthalmology & Visual Science
|January 26, 2019
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Summary
This summary is machine-generated.

This study introduces an AI-driven archetype method to detect visual field progression. The new method accurately identifies patterns of vision loss, aiding clinical decisions.

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Visual field (VF) defects are crucial indicators of glaucoma progression.
  • Accurate detection of VF progression is essential for timely intervention and management.
  • Current methods for assessing VF progression have limitations in sensitivity and specificity.

Purpose of the Study:

  • To develop and validate an artificial intelligence (AI)-based archetype method for detecting visual field (VF) progression by analyzing spatial pattern changes.
  • To compare the performance of the archetype method against established metrics like mean deviation (MD) slope, Advanced Glaucoma Intervention Study (AGIS) scoring, Collaborative Initial Glaucoma Treatment Study (CIGTS) scoring, and permutation of pointwise linear regression (PoPLR).

Main Methods:

  • 12,217 eyes from 7360 patients with at least five reliable 24-2 VFs and 5 years of follow-up were analyzed.
  • Visual fields were decomposed into 16 AI-derived archetype patterns.
  • Linear regressions were applied to archetype weights over time to define progression based on normal archetype decrease or defect archetype increase outside normal limits.
  • The archetype method was validated against established metrics and expert assessments.

Main Results:

  • The archetype method demonstrated higher agreement with MD slope (kappa: 0.37) and PoPLR (0.33) compared to AGIS (0.12) and CIGTS (0.22) in the development cohort.
  • The most frequent progression patterns included decreased normal pattern (63.7%), nasal steps (16.4%), and altitudinal loss (15.9%).
  • In the validation cohort, the archetype method (kappa: 0.51, accuracy: 0.77) significantly outperformed AGIS, CIGTS, MD slope, and PoPLR (P < 0.001).

Conclusions:

  • The AI-driven archetype method provides a robust and accurate approach for detecting visual field progression.
  • This method can inform clinicians about specific patterns of vision loss, facilitating better patient management.
  • The archetype method shows significant potential to improve the monitoring of glaucoma and other conditions causing visual field defects.