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

You might also read

Related Articles

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

Sort by
Same author

A macrophage-targeted bivalent saRNA vaccine protects against ocular and cutaneous HSV-1 infection in mice.

Journal of controlled release : official journal of the Controlled Release Society·2026
Same author

Multi-view Chest X-Ray Vision-Language Pre-training via Semantic-Aware Masked Language Modeling and High-order Alignment.

IEEE transactions on medical imaging·2026
Same author

Diffusion models for brain imaging computing: a survey of frameworks and applications.

Brain informatics·2026
Same author

Multimodal artificial intelligence in retinopathy of prematurity: A comprehensive narrative review.

Survey of ophthalmology·2026
Same author

Semi-URF: Progressive Uncertainty-Aware Region Filtering and Fusion for Semi-Supervised Medical Image Segmentation.

IEEE journal of biomedical and health informatics·2026
Same author

Edge-Defect Governed Graphene with In-Plane Conduction and Out-of-Plane Polarization Enables Microwave Absorption and Infrared Stealth.

Small (Weinheim an der Bergstrasse, Germany)·2026

Related Experiment Video

Updated: Sep 29, 2025

Application of Optical Coherence Tomography to a Mouse Model of Retinopathy
08:22

Application of Optical Coherence Tomography to a Mouse Model of Retinopathy

Published on: January 12, 2022

4.5K

Predicting Optical Coherence Tomography-Derived High Myopia Grades From Fundus Photographs Using Deep Learning.

Zhenquan Wu1, Wenjia Cai1, Hai Xie2

  • 1State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.

Frontiers in Medicine
|March 21, 2022
PubMed
Summary

An AI system can now predict high myopia grades from fundus photos, matching specialist accuracy. This technology aids in early detection of vision-threatening conditions, especially in underserved regions.

Keywords:
artificial intelligencedeep learningfundus photographshigh myopiaoptical coherence tomography

More Related Videos

In vivo Structural Assessments of Ocular Disease in Rodent Models using Optical Coherence Tomography
07:44

In vivo Structural Assessments of Ocular Disease in Rodent Models using Optical Coherence Tomography

Published on: July 24, 2020

3.1K
Using Optical Coherence Tomography and Optokinetic Response As Structural and Functional Visual System Readouts in Mice and Rats
07:08

Using Optical Coherence Tomography and Optokinetic Response As Structural and Functional Visual System Readouts in Mice and Rats

Published on: January 10, 2019

10.2K

Related Experiment Videos

Last Updated: Sep 29, 2025

Application of Optical Coherence Tomography to a Mouse Model of Retinopathy
08:22

Application of Optical Coherence Tomography to a Mouse Model of Retinopathy

Published on: January 12, 2022

4.5K
In vivo Structural Assessments of Ocular Disease in Rodent Models using Optical Coherence Tomography
07:44

In vivo Structural Assessments of Ocular Disease in Rodent Models using Optical Coherence Tomography

Published on: July 24, 2020

3.1K
Using Optical Coherence Tomography and Optokinetic Response As Structural and Functional Visual System Readouts in Mice and Rats
07:08

Using Optical Coherence Tomography and Optokinetic Response As Structural and Functional Visual System Readouts in Mice and Rats

Published on: January 10, 2019

10.2K

Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • High myopia poses a significant risk for vision-threatening complications.
  • Accurate grading of high myopia often requires advanced imaging like optical coherence tomography (OCT).
  • There is a need for accessible and efficient methods for myopia assessment.

Purpose of the Study:

  • To develop an artificial intelligence (AI) system for predicting high myopia grades using only fundus photographs.
  • To enable the detection of myopic maculopathy subtypes (atrophy, traction, neovascularization) from fundus images.

Main Methods:

  • A retrospective study utilized 1,853 fundus photographs.
  • A deep learning model was developed and trained on labeled fundus photographs.
  • The AI model's performance was compared against ophthalmologists and retinal specialists.

Main Results:

  • The AI model achieved high diagnostic performance with AUCs of 0.969 (atrophy), 0.895 (traction), and 0.936 (neovascularization).
  • Average accuracies for predicting categories A, T, and N were 92.38%, 85.34%, and 94.21%, respectively.
  • The AI system demonstrated performance comparable to retinal specialists and superior to attending ophthalmologists.

Conclusions:

  • An AI system can accurately predict vision-threatening conditions in high myopia using fundus photographs alone.
  • This AI tool offers a cost-effective alternative to OCT imaging for myopia assessment.
  • The system is particularly valuable for improving eye care accessibility in resource-limited settings.