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Updated: Sep 9, 2025
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Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential
Published on: May 25, 2020
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Fusing Structural Phenotypes with Functional Data for Early Prediction of Primary Angle-Closure Glaucoma Progression.
Swati Sharma1, Thanadet Chuangsuwanich2, Royston K Y Tan1,3
1Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.
Arxiv
|September 2, 2025
Summary
This study developed a method to classify primary angle closure glaucoma (PACG) patients as slow or fast progressors. Combining optic nerve head (ONH) structure and visual field (VF) data improved classification accuracy for glaucoma progression.
Area of Science:
- Ophthalmology
- Medical technology
- Artificial intelligence in medicine
Background:
- Primary angle closure glaucoma (PACG) is a leading cause of irreversible blindness worldwide.
- Accurate classification of glaucoma progression is crucial for timely intervention and management.
- Current methods for assessing glaucoma progression often lack integration of structural and functional parameters.
Purpose of the Study:
- To develop and validate an integrated approach for classifying eyes as slow or fast progressors in patients with PACG.
- To combine optic nerve head (ONH) structural features with sector-based visual field (VF) functional parameters for improved classification.
- To identify key predictors of glaucoma progression using machine learning.
Main Methods:
- A retrospective longitudinal study included PACG patients with at least 5 years of follow-up and 5 reliable VF tests.
- Artificial intelligence (AI) segmented OCT volumes to extract 31 ONH parameters.
- Sector-based VF data (mean sensitivity per region) were combined with structural parameters to train machine learning (ML) classifiers, with SHAP analysis for predictor identification.
Main Results:
- The study analyzed 451 eyes from 299 PACG patients. Mean VFI progression was -0.92% per year.
- A Random Forest model integrating structural and functional features achieved the highest classification performance (AUC = 0.87±0.02).
- Key predictors included inferior ONH features (MRW, RNFL thickness), nasal-temporal LC curvature, and superior nasal VF sensitivity.
Conclusions:
- Combining ONH structural and VF functional parameters significantly enhances the classification of glaucoma progression risk in PACG.
- Inferior ONH features, specifically MRW and RNFL thickness, were identified as the most predictive parameters.
- This integrated approach highlights the critical role of ONH morphology in monitoring PACG disease progression.
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