Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Glaucoma: Overview01:25

Glaucoma: Overview

1.1K
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...
1.1K
Open Angle Glaucoma: Treatment01:27

Open Angle Glaucoma: Treatment

885
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...
885

You might also read

Related Articles

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

Sort by
Same author

Diagnostic Performance of Prespecified OCT Rules for Glaucomatous Optic Neuropathy in Nonpathologic Myopia.

JAMA ophthalmology·2026
Same author

A Graph Neural Network-Based Multispectral-View Learning Model for Diabetic Macular Ischemia Detection From Color Fundus Photographs.

Translational vision science & technology·2026
Same author

An AI-Based OCT System to Detect Diabetic Macular Edema: A Prospective Validation and Noninferiority Randomized Clinical Trial.

JAMA·2026
Same author

Artificial intelligence-based retinal imaging for brain health assessment: a scoping review.

The Lancet. Digital health·2026
Same author

AI-Driven Oculomics-The Brain-Retina Connection.

JAMA ophthalmology·2026
Same author

Corrigendum to "Oculomics and AI: The eye as a biomarker for health span" [Asia-Pac J Ophthalmol 15 (1) (2026) 100282].

Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)·2026
Same journal

Co-assistant networks by pathology foundation model and convolutional neural network for gigapixel whole slide image analysis.

Medical image analysis·2026
Same journal

MBAS2024: A large-scale benchmark for multi-class bi-atrial segmentation in multi-center contrast-enhanced MRIs.

Medical image analysis·2026
Same journal

Respiratory motion augmentation for personalized super-resolution (RMApSR) of 3D cine MR images in MRI-guided radiotherapy.

Medical image analysis·2026
Same journal

Biom3d, a modular framework to host and develop 3D segmentation methods.

Medical image analysis·2026
Same journal

Embracing intra-class heterogeneity for semi-supervised medical image segmentation: From diversity to precision.

Medical image analysis·2026
Same journal

Real-time patient-specific microwave ablation zone prediction via a unified bioheat solver and MRI-informed perturbation learning.

Medical image analysis·2026
See all related articles

Related Experiment Video

Updated: Dec 20, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

3.2K

Towards multi-center glaucoma OCT image screening with semi-supervised joint structure and function multi-task

Xi Wang1, Hao Chen1, An-Ran Ran2

  • 1Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China.

Medical Image Analysis
|May 23, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method combining Optical Coherence Tomography (OCT) imaging and visual field data for glaucoma diagnosis. The approach effectively distinguishes glaucoma patients, showing high accuracy on large datasets.

Keywords:
Deep learningGlaucoma screeningOptical coherence tomographySemi-supervised multi-task learning

More Related Videos

Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT
12:22

Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT

Published on: August 4, 2018

8.8K
Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
07:12

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

Published on: April 11, 2025

782

Related Experiment Videos

Last Updated: Dec 20, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

3.2K
Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT
12:22

Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT

Published on: August 4, 2018

8.8K
Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
07:12

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

Published on: April 11, 2025

782

Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Glaucoma is a leading cause of irreversible blindness globally.
  • Accurate diagnosis relies on structure and function assessments, with Optical Coherence Tomography (OCT) gaining prominence for structural analysis.
  • Automated screening methods for glaucoma using OCT images remain limited.

Purpose of the Study:

  • To develop and validate an automated method for glaucoma screening by unifying structural analysis from OCT images and functional regression from visual field data.
  • To improve the accuracy of glaucoma diagnosis by simultaneously exploring the relationship between ocular structure and visual function.

Main Methods:

  • A semi-supervised learning strategy was employed for surrogate assignment of missing functional regression labels.
  • A multi-task learning network was developed to analyze OCT images and visual field measurements concurrently.
  • The method was evaluated on two large-scale, multi-center datasets: the HK dataset (975,400 B-scans) and the Stanford dataset (246,200 B-scans).

Main Results:

  • The proposed method achieved a volume-level Area Under the ROC Curve (AUC) of 0.977 on the HK dataset and 0.933 on the Stanford dataset.
  • Performance significantly surpassed baseline methods and human expert glaucoma diagnosis.
  • The model demonstrated robust performance without further fine-tuning on the independent Stanford dataset.

Conclusions:

  • The unified approach for structure and function analysis shows significant potential for automated glaucoma diagnosis systems.
  • This method offers a powerful tool for early and accurate detection of glaucoma, potentially reducing irreversible blindness.
  • The study established the largest glaucoma OCT image dataset to date, facilitating further research and development.