Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Deep Learning Meets InSAR for Infrastructure Monitoring: A Systematic Review of Models, Applications, and Challenges.

Sensors (Basel, Switzerland)·2025

Same author

Comparative Analysis of Transformer Architectures and Ensemble Methods for Automated Glaucoma Screening in Fundus Images from Portable Ophthalmoscopes.

Vision (Basel, Switzerland)·2025

Same author

Enhancing human activity recognition with machine learning: insights from smartphone accelerometer and magnetometer data.

PeerJ. Computer science·2025

Same author

Potential application of seaweed extract in tomato to improve bioactive compounds during postharvest storage.

Journal of food science and technology·2025

Same author

Enhancing Security in 5G Edge Networks: Predicting Real-Time Zero Trust Attacks Using Machine Learning in SDN Environments.

Sensors (Basel, Switzerland)·2025

Same author

Lower Limb Movement during 10-Meter Walk Test: A Dataset with Inertial, Magnetic, and Temperature Sensors.

Scientific data·2025

Search research articles

Related Experiment Video

Updated: Sep 5, 2025

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

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

Published on: May 25, 2020

Retinal Glaucoma Public Datasets: What Do We Have and What Is Missing?

José Camara^1,2, Roberto Rezende^1,2, Ivan Miguel Pires^2,3

¹Departamento de Ciências e Tecnologia, Universidade Aberta, 1250-100 Lisboa, Portugal.

Journal of Clinical Medicine

|July 9, 2022

Summary

This study addresses limitations in public glaucoma image databases, proposing improvements for automated screening using deep learning. Enhanced databases will aid early diagnosis and treatment, preventing blindness.

Keywords:

databases glaucoma glaucoma screening machine learning retinal images

More Related Videos

Laser Capture Microdissection of Highly Pure Trabecular Meshwork from Mouse Eyes for Gene Expression Analysis

Laser Capture Microdissection of Highly Pure Trabecular Meshwork from Mouse Eyes for Gene Expression Analysis

Published on: June 3, 2018

Using Retinal Imaging to Study Dementia

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

Related Experiment Videos

Last Updated: Sep 5, 2025

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

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

Published on: May 25, 2020

Laser Capture Microdissection of Highly Pure Trabecular Meshwork from Mouse Eyes for Gene Expression Analysis

Laser Capture Microdissection of Highly Pure Trabecular Meshwork from Mouse Eyes for Gene Expression Analysis

Published on: June 3, 2018

Using Retinal Imaging to Study Dementia

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Public databases for glaucoma research contain retinal images, primarily of the optic papilla, for automated screening using deep learning.
Current databases suffer from heterogeneity in image acquisition, lack of optic papilla segmentation, and insufficient data for early diagnosis.
Existing datasets are not interchangeable and use varied classification criteria, hindering reliable deep learning model development.

Purpose of the Study:

To propose improvements in the structure and presentation of public databases for automated screening of glaucomatous papillae.
To enhance existing datasets with relevant medical information and standardized segmentation.
To facilitate the development of robust deep learning models for early glaucoma detection.

Main Methods:

Analysis of existing public glaucoma image databases and their limitations.
Development of a structured approach for presenting retinal images with optic papilla and cupping segmentation.
Inclusion of medical context and standardized classification criteria for glaucoma diagnosis.

Main Results:

Identified critical shortcomings in current public glaucoma databases, including data heterogeneity and lack of standardization.
Proposed a framework for enhanced database structure, incorporating expert-made segmentations and clear classification guidelines.
Highlighted the need for simultaneous imaging of both eyes and early diagnostic markers.

Conclusions:

Improving public glaucoma databases is crucial for advancing automated screening and early diagnosis.
Standardized, well-annotated datasets are essential for training reliable deep learning models for glaucoma detection.
Enhanced databases will support large-scale population screening, reduce diagnostic ambiguity, and promote timely treatment to prevent blindness.