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...

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...

You might also read

Related Articles

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

Sort by

Same author

Explainable Machine Learning for Predicting Dengue Recovery Duration: Insights from Multi-Center Clinical Data.

Healthcare (Basel, Switzerland)·2026

Same author

Gestational weight gain and maternal immediate perinatal and postpartum outcomes in low and middle income countries: individual participant data meta-analyses.

BMJ medicine·2026

Same author

Highly stable photothermal CO<sub>2</sub> conversion to C<sub>2</sub>H<sub>6</sub> with 87% selectivity: cooperative charge redistribution in a heteronuclear catalyst stabilizes the reaction intermediates and enhances C-C coupling.

Materials horizons·2026

Same author

Atomically dispersed cobalt on niobium oxide enables photothermal nitrogen fixation via nitrogen-hydrogen coupling.

Journal of colloid and interface science·2026

Same author

Letter to the Editor on "Clinical profile and optical coherence tomography based morphological characteristics of coronary in-stent restenosis: A cross-sectional study".

Indian heart journal·2026

Same author

Optimized host adaptation to Eri silkworm eggs enhances fitness and biological control efficacy of Trichogramma ostriniae against the Asian corn borer.

Pest management science·2026

Search research articles

Related Experiment Video

Updated: Jan 16, 2026

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

Adaptive Multimodal Fusion in Vertical Federated Learning for Decentralized Glaucoma Screening.

Ayesha Jabbar^1,2, Jianjun Huang^1,2, Muhammad Kashif Jabbar^1,2

¹College of Electronics and Information Engineering, Shenzhen University, Shenzhen 518060, China.

|September 27, 2025

Summary

This study introduces a new Quality Aware Vertical Federated Learning (QAVFL) framework for decentralized glaucoma detection using multimodal data. QAVFL achieves high accuracy in detecting glaucoma while preserving patient privacy across fragmented healthcare systems.

Keywords:

attention-based neural networks federated learning glaucoma detection multimodal fusion privacy-preserving healthcare AI vertical

More Related Videos

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

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

Published on: April 11, 2025

Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

Published on: July 21, 2020

Related Experiment Videos

Last Updated: Jan 16, 2026

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

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

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

Published on: April 11, 2025

Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

Published on: July 21, 2020

Area of Science:

Ophthalmology
Medical Imaging
Machine Learning
Data Privacy

Background:

Early glaucoma detection is crucial for preventing vision loss.
Unimodal retinal imaging has limitations in accuracy and context.
Fragmented clinical data and privacy regulations hinder centralized AI development.

Purpose of the Study:

To develop a decentralized multimodal glaucoma detection framework.
To address data sparsity and privacy challenges in glaucoma diagnosis.
To improve the accuracy and robustness of glaucoma screening.

Main Methods:

Proposed a Quality Aware Vertical Federated Learning (QAVFL) framework.
Integrated clinical text, retinal images, and biomedical signals using modality-specific encoders.
Employed a Fusion Attention Module (FAM) for adaptive multimodal weighting.
Utilized homomorphic encryption and differential privacy for secure aggregation.

Main Results:

QAVFL achieved 98.6% accuracy, 98.6% recall, 97.0% F1-score, and 0.992 AUC.
Demonstrated statistically significant improvements over unimodal and early fusion methods (p < 0.01).
Validated performance in heterogeneous non-IID settings.

Conclusions:

Dynamic multimodal fusion is effective for privacy-preserving decentralized learning.
QAVFL offers a scalable and clinically applicable solution for glaucoma screening.
The framework addresses challenges of fragmented healthcare data for AI-driven diagnostics.