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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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Detecting Glaucoma from Fundus Photographs Using Deep Learning without Convolutions: Transformer for Improved

Rui Fan^1,2,3, Kamran Alipour², Christopher Bowd¹

¹Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, California.

Ophthalmology Science

|December 22, 2022

Summary

This summary is machine-generated.

The Data-efficient image Transformer (DeiT) demonstrated superior performance and better explainability in detecting primary open-angle glaucoma (POAG) compared to ResNet-50 on external datasets. This deep learning approach shows promise for improving diagnostic accuracy in eye diseases.

Keywords:

AI, artificial intelligence AUROC, areas under the receiver operating characteristic curve CI, confidence interval CNN, convolutional neural network DL, deep learning Deep learning DeiT, Data-efficient image Transformer Fundus photographs Glaucoma detection LAG, Large-Scale Attention-Based Glaucoma OHTS, Ocular Hypertension Treatment Study POAG, primary open-angle glaucoma SoTA, state-of-the-art VF, visual field ViT, Vision Transformer Vision Transformers

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Area of Science:

Ophthalmology
Artificial Intelligence
Medical Imaging

Background:

Deep learning models are increasingly used for medical image analysis.
Accurate detection of primary open-angle glaucoma (POAG) is crucial for preventing vision loss.
Existing models may lack generalizability and explainability.

Purpose of the Study:

To compare the diagnostic accuracy and explainability of Data-efficient image Transformer (DeiT) and ResNet-50 for detecting POAG using fundus photographs.
To identify critical image regions influencing model decisions.

Main Methods:

Trained DeiT and ResNet-50 models on Ocular Hypertension Treatment Study (OHTS) fundus images for 5 POAG classifications.
Compared model performance using Area Under the Receiver Operating Characteristic Curve (AUROC) and sensitivity at fixed specificities.
Assessed model explainability via attention maps and gradient-weighted class activation mapping.

Main Results:

DeiT models showed similar performance to ResNet-50 on OHTS test sets (AUROC 0.82-0.91).
DeiT consistently outperformed ResNet-50 on 5 external datasets, with AUROC improvements of 0.08-0.20.
DeiT's attention maps highlighted localized neuroretinal rim features, while ResNet-50 maps were more diffuse.

Conclusions:

Vision Transformers, like DeiT, offer potential for enhanced generalizability and explainability in deep learning for medical imaging.
These findings suggest a promising role for DeiT in diagnosing eye diseases and potentially other conditions reliant on imaging.