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Updated: Jun 13, 2025
07:11
Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential
Published on: May 25, 2020
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Using Multi-Layer Perceptron Driven Diagnosis to Compare Biomarkers for Primary Open Angle Glaucoma.
Nicholas Riina1, Alon Harris1, Brent A Siesky1
1Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, New York, United States.
Investigative Ophthalmology & Visual Science
|September 9, 2024
Summary
Neural network models effectively identified ocular biomarkers for diagnosing primary open-angle glaucoma (POAG). Vascular and structural features from OCT and OCTA imaging showed high diagnostic accuracy, comparable to traditional metrics.
Area of Science:
- Ophthalmology
- Medical Imaging
- Artificial Intelligence
Background:
- Primary open-angle glaucoma (POAG) is a leading cause of irreversible blindness.
- Early diagnosis and accurate identification of biomarkers are crucial for effective POAG management.
- Current diagnostic methods may benefit from advanced analytical tools for improved accuracy.
Purpose of the Study:
- To employ neural network machine learning (ML) models to pinpoint the most effective ocular biomarkers for diagnosing POAG.
- To compare the diagnostic utility of various ocular biomarkers, including structural and vascular features, in conjunction with traditional parameters.
Main Methods:
- Neural network models (multi-layer perceptrons) were trained on a dataset of 93 POAG patients and 113 controls.
- Models incorporated base parameters (intraocular pressure, blood pressure, heart rate, visual field) plus specific biomarkers: structural (optic nerve, RNFL, GCC, macular thickness), choroidal, or vascular features via OCT and OCTA.
- Model performance was evaluated using tenfold cross-validation and compared via AUC analysis.
Main Results:
- Both vascular and structural biomarker models demonstrated significantly higher diagnostic accuracy than the base model.
- The hemodynamic model achieved an AUC of 0.819, while the structural model achieved 0.816, indicating comparable high performance.
- Models incorporating GCC + RNFL thickness and combined structural/vascular features also showed significant improvements over the base model.
Conclusions:
- Neural network models suggest that OCT angiography (OCTA) vascular biomarkers of the optic nerve head are as valuable for POAG diagnosis as OCT structural biomarkers.
- Machine learning approaches can effectively integrate multimodal ocular imaging data for enhanced POAG diagnosis.
- This study highlights the potential of combining vascular and structural imaging data for robust POAG detection.
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