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Related Experiment Video

Updated: Jun 6, 2026

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential

Published on: May 25, 2020

Closed angle glaucoma detection in RetCam images.

Jun Cheng¹, Jiang Liu, Beng Hai Lee

¹Institute for Infocomm Research, A*STAR, 1 Fusionopolis Way, #21-01 Connexis, Singapore 138632. jcheng@i2r.a-star.edu.sg

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|November 25, 2010

Summary

This study introduces an automated system for classifying open-angle versus closed-angle glaucoma using RetCam images. The intelligent analysis aims to improve diagnostic speed and accuracy for this crucial eye condition.

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Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Glaucoma diagnosis relies on classifying angle-closure versus open-angle types.
Retinal Camera (RetCam) imaging offers a novel method for visualizing the iridocorneal angle.
Manual analysis of RetCam images is time-consuming and prone to subjective interpretation.

Purpose of the Study:

To develop an automated system for classifying closed-angle glaucoma from open-angle glaucoma using RetCam images.
To compare the performance of two distinct computational approaches for automated classification.

Main Methods:

Utilized RetCam imaging to capture iridocorneal angle images.
Developed and implemented two distinct computational methods for automated image analysis and classification.
Evaluated and compared the performance metrics of the proposed classification approaches.

Main Results:

The proposed automated system demonstrated promising results in differentiating closed-angle from open-angle glaucoma.
Experimental outcomes indicate the potential for accurate and efficient classification.
Comparative analysis highlighted the performance differences between the two proposed methods.

Conclusions:

Automated analysis of RetCam images offers a viable solution for glaucoma classification.
The developed intelligent system can assist clinicians in diagnosing glaucoma subtypes more efficiently.
Further research and validation are warranted to fully integrate this technology into clinical practice.