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

Search research articles

Related Experiment Video

Updated: Dec 23, 2025

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Artificial Intelligence for Cataract Detection and Management.

Jocelyn Hui Lin Goh^1,2, Zhi Wei Lim^1,3, Xiaoling Fang^1,4

¹Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.

Asia-Pacific Journal of Ophthalmology (Philadelphia, Pa.)

|April 30, 2020

Summary

Related Concept Videos

You might also read

Related Articles

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

Sort by

Same author

Role of C-Reactive Protein as a Predictor of Difficult Laparoscopic Cholecystectomy.

Surgical innovation·2023

Same author

Detecting visually significant cataract using retinal photograph-based deep learning.

Nature aging·2023

Same author

Author Correction: Detecting visually significant cataract using retinal photograph-based deep learning.

Nature aging·2023

Same author

Visual Impairment, Major Eye Diseases, and Mortality in a Multi-Ethnic Asian Population and a Meta-analysis of Prospective Studies.

American journal of ophthalmology·2021

Same author

Global Prevalence of Diabetic Retinopathy and Projection of Burden through 2045: Systematic Review and Meta-analysis.

Ophthalmology·2021

Same author

Visual field defects and myopic macular degeneration in Singapore adults with high myopia.

The British journal of ophthalmology·2021

Same journal

Benchmarking and fine-tuning vision-language models on a visual question answering dataset for myopic maculopathy.

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

Same journal

Does lens opacity matter? The effect of cataract on deep learning based cardiovascular disease risk scores from fundus photos.

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

Same journal

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

Visual preservation and surgical outcomes of phacogoniotomy in end-stage glaucoma: A multicenter study.

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

Same journal

Association of asymmetrical normal tension glaucoma, obstructive sleep apnoea and side sleeping.

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

Same journal

Mechanism of myopic axial elongation related to Bruch´s membrane.

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

See all related articles

This summary is machine-generated.

Artificial intelligence (AI) is increasingly used in ophthalmology, but cataract research lags behind other eye diseases. Future AI development requires better data and validation for clinical use.

Area of Science:

Ophthalmology
Artificial Intelligence
Medical Imaging

Background:

Artificial intelligence (AI) is rapidly advancing in ophthalmology, driven by large clinical datasets.
Cataract is a leading cause of visual impairment globally, yet AI development for cataract is less explored than for other age-related eye diseases.
Existing AI applications in cataract include automated assessment from images and intraocular lens power calculation for surgery.

Purpose of the Study:

To review the current state of AI development in cataract.
To identify limitations in current AI research for cataract, such as data quality and validation.
To outline future directions for AI in cataract, focusing on performance, deployment, and cost-effectiveness.

Main Methods:

Review of previous studies on AI algorithms for cataract assessment and intraocular lens power calculation.

More Related Videos

Author Spotlight: Unraveling the Molecular Mechanisms in PCO and Fibrosis Following Cataract Surgery

Author Spotlight: Unraveling the Molecular Mechanisms in PCO and Fibrosis Following Cataract Surgery

Published on: December 1, 2023

Artificial Intelligence Approaches to Assessing Primary Cilia

Artificial Intelligence Approaches to Assessing Primary Cilia

Published on: May 1, 2021

Related Experiment Videos

Last Updated: Dec 23, 2025

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Author Spotlight: Unraveling the Molecular Mechanisms in PCO and Fibrosis Following Cataract Surgery

Author Spotlight: Unraveling the Molecular Mechanisms in PCO and Fibrosis Following Cataract Surgery

Published on: December 1, 2023

Artificial Intelligence Approaches to Assessing Primary Cilia

Artificial Intelligence Approaches to Assessing Primary Cilia

Published on: May 1, 2021

Analysis of current trends in clinical data digitization and curation for AI development.

Identification of limitations and challenges in existing AI research for cataract.

Main Results:

AI development for cataract is less advanced compared to other major ophthalmic conditions.
Previous studies show promising early performance but are limited by insufficient high-quality training data and lack of external validation.
Advancements in data digitization are expected to improve future AI development for cataract.

Conclusions:

AI holds significant potential for cataract management, but current development is underexplored.
Addressing limitations in data quality and validation is crucial for robust AI systems.
Future research should evaluate not only algorithm performance but also the practical deployment, feasibility, efficiency, and cost-effectiveness of AI solutions in cataract care.