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Convolutional Graph Isomorphism Network to Detect Glaucomatous Visual Field Defects.

Douglas R da Costa1,2, Dániel Unyi3, Rafael Scherer1,2

  • 1Bascom Palmer Eye Institute, University of Miami, Miami, Florida.

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|February 5, 2026
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Summary
This summary is machine-generated.

A novel deep learning model using graph isomorphism networks (GINs) significantly improved the detection of glaucomatous visual field defects over traditional methods. This AI approach offers superior accuracy and interpretability for diagnosing glaucoma.

Keywords:
Deep learningGlaucomatous visual field defectsGraph isomorphism networkGraph neural networksStandard automated perimetry

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Area of Science:

  • Ophthalmology and Computational Vision
  • Artificial Intelligence in Medical Diagnostics

Background:

  • Glaucomatous visual field defects are a leading cause of irreversible blindness.
  • Accurate and early detection of these defects is crucial for timely intervention and management.
  • Current diagnostic methods, including standard automated perimetry (SAP) criteria, have limitations in sensitivity and specificity.

Purpose of the Study:

  • To evaluate a deep learning (DL) model based on graph isomorphism networks (GINs) for detecting glaucomatous visual field defects using 24-2 SAP data.
  • To compare the GIN model's performance against traditional diagnostic criteria (Anderson, GHT/PSD), a dense neural network (NN), and a convolutional neural network (CNN).

Main Methods:

  • A retrospective cross-sectional study analyzed 1874 reliable SAP tests from 676 patients.
  • A GIN model was developed, treating SAP data as graphs with node features including sensitivity and deviation values.
  • Performance was evaluated using metrics like AUC, sensitivity, and precision, comparing GIN against traditional criteria and other DL models.

Main Results:

  • The GIN model achieved a superior Area Under the Curve (AUC) of 0.982, significantly outperforming Anderson criteria (0.906), GHT/PSD (0.936), NN (0.941), and CNN (0.941).
  • At 95% specificity, the GIN model demonstrated the highest sensitivity (94.1%), surpassing other methods.
  • Explainability analysis confirmed the GIN model focuses on clinically relevant glaucomatous damage regions, offering better interpretability.

Conclusions:

  • Modeling SAP data as graphs using GINs provides superior diagnostic performance and interpretability for detecting glaucomatous visual field defects.
  • The GIN model represents a promising advancement for accurate and explainable glaucoma diagnosis in clinical settings.
  • This graph-based deep learning approach enhances the detection capabilities beyond conventional criteria and standard neural networks.