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Glaucoma: Overview

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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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Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.

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

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Deep Learning-Based Glaucoma Detection Using Clinical Notes: A Comparative Study of Long Short-Term Memory and

Ali Mohammadjafari¹, Maohua Lin², Min Shi¹

¹School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, LA 70504, USA.

Diagnostics (Basel, Switzerland)

|April 12, 2025

Summary

Deep learning models can detect glaucoma from clinical notes. Convolutional Neural Networks (CNNs) showed promising results with reduced bias across racial groups, highlighting the need for equitable AI in eye care.

Keywords:

AI healthcare CNN LSTM clinical notes deep learning fairness-aware modeling glaucoma detection

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Last Updated: May 14, 2025

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

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Published on: May 25, 2020

Area of Science:

Ophthalmology and Artificial Intelligence

Background:

Glaucoma is a leading cause of irreversible blindness worldwide.
Current detection methods often rely on retinal imaging, but the utility of clinical notes is less explored.

Purpose of the Study:

To investigate deep learning models' capability in detecting glaucoma from clinical notes.
To compare performance and fairness of different deep learning architectures.

Main Methods:

Compared Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNNs), BERT, and BioBERT models.
Utilized a real-world dataset of 10,000 patients' clinical notes.
Assessed model performance and group disparities across demographic categories.

Main Results:

CNN achieved an AUC of 0.80, slightly outperforming LSTM.
Both models exhibited performance disparities across racial groups.
CNN demonstrated reduced group disparities compared to LSTM, indicating more equitable outcomes.

Conclusions:

Deep learning models show potential for glaucoma detection using clinical notes.
Fairness-aware AI development is crucial to mitigate health disparities in glaucoma detection.