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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...

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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.
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Angle-closure glaucoma, or closed-angle glaucoma, is an eye condition where the iris bulges out and blocks the iridocorneal angle, resulting in a buildup of aqueous humor and increased intraocular pressure. Immediate medical attention is necessary due to the sudden onset of symptoms. The treatment for angle-closure glaucoma includes short-term and long-term approaches. Short-term treatment involves using eye drops like pilocarpine to lower intraocular pressure by increasing aqueous humor...

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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An Efficient Deep Learning Approach to Automatic Glaucoma Detection Using Optic Disc and Optic Cup Localization.

Marriam Nawaz¹, Tahira Nazir¹, Ali Javed¹

¹Department of Computer Science, University of Engineering and Technology Taxila, Rawalpindi 47050, Pakistan.

Sensors (Basel, Switzerland)

|January 22, 2022

Summary

This summary is machine-generated.

This study introduces an automated Deep Learning framework for early glaucoma detection, improving accuracy in identifying optic disc and optic cup lesions. The EfficientDet-D0 model demonstrates superior performance in classifying glaucoma from retinal images.

Keywords:

EfficientDet EfficientNet fundus images glaucoma

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Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Glaucoma, a leading cause of irreversible blindness, results from elevated intraocular pressure.
Timely screening and treatment are crucial to prevent vision loss, but manual analysis of retinal samples is resource-intensive and prone to delays.
Automated systems are needed to accurately identify early glaucomatous changes like optic disc and optic cup lesions, overcoming challenges posed by lesion variability and similarity to eye color.

Purpose of the Study:

To develop and validate an automated Deep Learning framework for accurate and early localization and classification of glaucoma from retinal fundus images.
To address the limitations of manual screening, including resource constraints and potential delays, thereby reducing the global vision loss ratio.

Main Methods:

A Deep Learning approach, EfficientDet-D0 with EfficientNet-B0 as the backbone, was employed for glaucoma detection.
The framework involves feature extraction using EfficientNet-B0, feature fusion via the Bi-directional Feature Pyramid Network (BiFPN) module, and final prediction of glaucomatous lesions.
Model robustness was evaluated on the ORIGA dataset and cross-validated on HRF and RIM ONE DL datasets.

Main Results:

The EfficientDet-D0 framework demonstrated high proficiency in localizing and classifying glaucoma lesions.
Quantitative and visual evaluations confirmed the model's effectiveness.
Cross-dataset validation highlighted the framework's generalization ability on diverse retinal image datasets.

Conclusions:

The proposed EfficientDet-D0 framework offers a robust and accurate automated solution for early glaucoma screening.
This Deep Learning approach outperforms existing methods, showing significant potential for improving glaucoma diagnosis and preventing vision loss.
The study underscores the efficacy of advanced AI in medical image analysis for critical eye diseases.