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...

Angle Closure Glaucoma: Treatment

Angle Closure Glaucoma: Treatment

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...

You might also read

Related Articles

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

Sort by

Same author

Modified Pemberton osteotomy for developmental dysplasia of the hip in children: a mid- to long-term follow-up study.

Frontiers in pediatrics·2026

Same author

Secreted ORF8 reprograms macrophages to enhance SARS-CoV-2 infection of lung epithelial cells.

bioRxiv : the preprint server for biology·2026

Same author

Self-adaptive hetero-phase superlattices in TaS<sub>2</sub> via layer-resolved 1T-to-1H transformations.

National science review·2026

Same author

Pixel-Level RGBT Fusion Tracking via Heterogeneous Multi-Expert Distillation and Decoupled Representation Learning.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same author

Effect and influencing factors of early ileostomy closure 2-4 weeks on patients with rectal cancer.

Frontiers in oncology·2026

Same author

Current-induced creation and dynamics of embedded magnetic skyrmion bags.

Nature communications·2026

Same journal

Reduced mechanical strength correlates with decreased elastin content in aortic intima-media tissue: association with dissection in human ascending aortas.

Medical & biological engineering & computing·2026

Same journal

How plaque morphology and stenosis severity govern stent-artery interaction and deployment outcomes: a computational study.

Medical & biological engineering & computing·2026

Same journal

Investigating a relation between amyloid beta plaque burden and accumulated neurotoxicity caused by amyloid beta oligomers.

Medical & biological engineering & computing·2026

Same journal

A robot-assisted eye positioning method with high precision and repeatability for ocular particle therapy: mechanical and geometric assessment.

Medical & biological engineering & computing·2026

Same journal

Enhanced puncture event detection for teleoperated needle insertion robotic system.

Medical & biological engineering & computing·2026

Same journal

Energy-efficient real-time 4-stage sleep classification at 10-second resolution.

Medical & biological engineering & computing·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Dec 11, 2025

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

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

Published on: November 30, 2022

Automated glaucoma screening method based on image segmentation and feature extraction.

Fan Guo¹, Weiqing Li¹, Jin Tang²

¹School of Automation, Central South University, Changsha, 410083, China.

Medical & Biological Engineering & Computing

|August 22, 2020

Summary

This summary is machine-generated.

This study introduces a new automatic glaucoma screening method combining clinical and image features. The approach achieves high accuracy in detecting glaucoma, crucial for preventing vision loss.

Keywords:

Feature extraction Glaucoma screening Image segmentation Neural network

More Related Videos

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

Using Retinal Imaging to Study Dementia

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

Related Experiment Videos

Last Updated: Dec 11, 2025

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

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

Published on: November 30, 2022

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

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:

Glaucoma is a leading cause of irreversible blindness.
Early detection of glaucoma is critical due to the lack of a cure.
Current screening methods can be improved with advanced technology.

Purpose of the Study:

To develop a novel, automated glaucoma screening method.
To integrate clinical measurement features with image-based features for enhanced accuracy.
To improve early detection rates for glaucoma.

Main Methods:

An improved UNet++ network was used for optic disc and optic cup segmentation.
Clinical features (e.g., optic cup-to-disc ratio) were extracted.
The increasing field of view (IFOV) model extracted image-based features.
Adaptive synthetic sampling addressed data imbalance.
A gradient boosting decision tree (GBDT) classifier was trained.

Main Results:

The proposed method achieved high performance on the ORIGA dataset.
Sensitivity: 0.894, Accuracy: 0.843, AUC: 0.901.
Outperformed existing glaucoma screening methods.

Conclusions:

The combined feature approach offers excellent glaucoma screening performance.
The novel method demonstrates superior accuracy and potential for early detection.
This automated system can aid in preventing glaucoma-induced blindness.