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Updated: Jul 6, 2026
07:11
Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential
Published on: May 25, 2020
Bayesian machine learning classifiers for combining structural and functional measurements to classify healthy and
Christopher Bowd1, Jiucang Hao, Ivan M Tavares
1Hamilton Glaucoma Center, Department of Ophthalmology, University of California, San Diego, La Jolla, CA 92037-0946, USA. cbowd@eyecenter.ucsd.edu
Investigative Ophthalmology & Visual Science
|March 11, 2008
Summary
Combining optical coherence tomography (OCT) and standard automated perimetry (SAP) measurements with machine learning classifiers (MLCs) slightly improved glaucoma detection accuracy. This approach enhances diagnostic performance for identifying glaucomatous eyes compared to using each method alone.
Area of Science:
- Ophthalmology
- Medical Imaging
- Machine Learning
Background:
- Glaucoma diagnosis relies on structural and functional measurements.
- Optical coherence tomography (OCT) provides structural data, while standard automated perimetry (SAP) offers functional data.
- Machine learning classifiers (MLCs) can analyze complex datasets for diagnostic purposes.
Purpose of the Study:
- To evaluate if combining OCT and SAP measurements improves diagnostic accuracy for glaucoma detection using MLCs.
- To compare the diagnostic performance of MLCs using combined OCT and SAP data versus individual data sources.
Main Methods:
- Sixty-nine healthy eyes and 156 glaucomatous eyes were analyzed.
- OCT (RNFL thickness) and SAP (pattern deviation values) data were collected.
- Relevance vector machine (RVM) and subspace mixture of Gaussians (SSMoG) MLCs were trained and tested using tenfold cross-validation.
Main Results:
- The area under the receiver operating characteristic curve (AUROC) for combined OCT and SAP data ranged from 0.845 (RVM) to 0.869 (SSMoG).
- Combining OCT and SAP data marginally improved diagnostic performance compared to using either OCT or SAP alone.
- Both RVM and SSMoG showed statistically similar classification performance.
Conclusions:
- Bayesian MLCs (RVM and SSMoG) trained on combined OCT and SAP data can effectively distinguish between healthy and early glaucomatous eyes.
- Integrating OCT and SAP measurements offers a marginal enhancement in diagnostic performance for glaucoma detection.
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