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Glaucoma: Overview

Glaucoma: Overview

Glaucoma is an eye condition characterized by increased intraocular pressure that damages the retina and optic nerve, leading to irreversible blindness if left untreated. The human eye has various components, including the cornea, iris, pupil, lens, and optic nerve. Aqueous humor is secreted by the epithelium of the ciliary body in the posterior chamber and flows through the trabecular meshwork and canal of Schlemm, maintaining normal intraocular pressure. The trabecular meshwork and the canal...

Open Angle Glaucoma: Treatment

Open Angle Glaucoma: Treatment

In open-angle glaucoma, the iridocorneal angle remains open, but the trabecular meshwork becomes stiff, slowing down the outflow of aqueous humor. This causes a buildup of aqueous humor in the anterior chamber, leading to a sudden increase in intraocular pressure. The treatment for open-angle glaucoma focuses on reducing the elevated intraocular pressure by either decreasing the secretion of aqueous humor or increasing its outflow.
Drugs such as carbonic anhydrase inhibitors, α2- and...

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Related Experiment Video

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DDLSNet: A Novel Deep Learning-Based System for Grading Funduscopic Images for Glaucomatous Damage.

Haroon Adam Rasheed¹, Tyler Davis², Esteban Morales³

¹University of California Los Angeles David Geffen School of Medicine, Los Angeles, California.

Ophthalmology Science

|January 9, 2023

Summary

This study introduces DDLSNet, an automated image analysis pipeline for glaucoma assessment. DDLSNet shows moderate agreement with clinicians in grading disc damage likelihood, demonstrating feasibility for automated optic disc photograph analysis.

Keywords:

ARW, absent rim width CI, confidence interval DDLS DDLS, disc damage likelihood scale DiscNet, disc size classification model Glaucoma MAE, mean average error Neural network ODP, optic disc photograph RimIoU, rim intersection over union RimNet, rim segmentation model Segmentation mRDR, minimum rim-to-disc ratio

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Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Glaucoma assessment relies on manual grading of optic disc photographs (ODPs).
Accurate estimation of optic disc size and rim width is crucial for glaucoma diagnosis.
Automating this process can improve efficiency and consistency in clinical practice.

Purpose of the Study:

To develop and evaluate DDLSNet, an automated image analysis pipeline for estimating the disc damage likelihood scale (DDLS).
To assess the performance of DDLSNet's components: RimNet for rim segmentation and DiscNet for disc size classification.

Main Methods:

DDLSNet integrates RimNet (InceptionV3/LinkNet) and DiscNet (VGG19) for ODP analysis.
Datasets included 1208 ODPs for RimNet and 11,536 for DiscNet, with 120 ODPs for performance evaluation.
Ground truth for rim width and disc size was established using clinician markings and OCT images, respectively.

Main Results:

RimNet achieved low mean absolute errors for rim width estimation (mRDR: 0.04, ARW: 48.9 degrees).
DiscNet achieved 73% classification accuracy for optic disc size.
DDLSNet demonstrated moderate agreement (weighted kappa: 0.54) with clinician grading, comparable to interclinician agreement (0.52).

Conclusions:

DDLSNet achieves moderate agreement with clinicians for automated DDLS grading.
This automated pipeline shows promise for assessing glaucoma severity using ODPs.
Future work could enhance performance by increasing sample sizes and improving clinician grading consistency.