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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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Glaucoma detection in Latino population through OCT's RNFL thickness map using transfer learning.

Liza G Olivas¹, Germán H Alférez², Javier Castillo³

¹School of Engineering and Technology, Universidad de Montemorelos, Montemorelos, NL, Mexico.

International Ophthalmology

|July 2, 2021

Summary

Deep learning models, MobileNet and Inception V3, show promise for detecting glaucoma using optical coherence tomography (OCT) retinal nerve fiber layer thickness maps in Mexican patients. These AI tools can aid in early diagnosis to prevent blindness.

Keywords:

Convolutional neural networks Deep learning Glaucoma Inception V3 Latino population MobileNet Optical coherence tomography Retinal nerve fiber layer Thickness map Transfer learning

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Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Glaucoma is a leading cause of irreversible blindness globally, affecting over 60 million people.
Early diagnosis is crucial for preventing vision loss, yet many cases remain undiagnosed.
Deep learning (DL) offers potential for objective glaucoma detection, but research in Latino populations is limited.

Purpose of the Study:

To investigate the efficacy of transfer learning using MobileNet and Inception V3 models for glaucoma detection.
To analyze optical coherence tomography (OCT) images of retinal nerve fiber layer (RNFL) thickness maps from Mexican patients.
To address the gap in DL applications for glaucoma screening in the Latino population.

Main Methods:

Utilized the IBM Foundational Methodology for Data Science.
Employed MobileNet and Inception V3 architectures for classifying OCT images into glaucomatous and non-glaucomatous categories.
Retrained models using transfer learning on RNFL thickness map images from 333 OCT files, with 50 images per class for training and 15 per class for prediction.

Main Results:

MobileNet achieved 86% accuracy for the left eye and 90% for the right eye.
Inception V3 demonstrated 90% accuracy for both the left and right eyes.
Both models showed high precision, recall, and F1 scores, indicating strong performance in glaucoma detection.

Conclusions:

Transfer learning with MobileNet and Inception V3 models is effective for glaucoma detection using OCT RNFL images.
Inception V3 slightly outperformed MobileNet in classifying left eye images.
The models demonstrated high accuracy, suggesting their potential as diagnostic aids for ophthalmologists.