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

Vision01:24

Vision

52.9K
Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
52.9K

You might also read

Related Articles

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

Sort by
Same author

Pressure-induced softening of locust bean gum hydrogels: A counterintuitive alternative to freeze-thaw stiffening.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Semaglutide and Neovascular Age-Related Macular Degeneration Among Adults with Type 2 Diabetes: An OHDSI Network Study.

Ophthalmology·2026
Same author

Impact of Concurrent Graft-versus-Host Disease on Long-Term Survival in Critically Ill Patients Following Allogeneic Hematopoietic Stem Cell Transplantation for Hematological Malignancies.

Transplantation and cellular therapy·2026
Same author

One Size Fits All? Comparing Foundation and Task-specific Models for Retinal Fluid Segmentation.

medRxiv : the preprint server for health sciences·2026
Same author

The Association of Vertically Integrated Delivery Networks on Lapses in Diabetic Retinopathy Care and Presenting Diabetic Retinopathy Severity.

Clinical ophthalmology (Auckland, N.Z.)·2026
Same author

Effects of Time from Diagnosis to Treatment and Baseline Vision on Retinal Vein Occlusion Outcomes in Aflibercept 2 mg Phase III Trials.

Ophthalmology. Retina·2026
Same journal

Notice of Retraction. Ren Y, et al. Personality Traits and Social Isolation in Older Adults. JAMA Netw Open. 2026;9(5):e269569.

JAMA network open·2026
Same journal

Error in Grant Number in Funding/Support Section.

JAMA network open·2026
Same journal

The Supplementary Role of Friends in Caregiving Networks.

JAMA network open·2026
Same journal

Urbanicity, Neighborhood Conditions, and Dementia Mortality.

JAMA network open·2026
Same journal

Equity and Cancer Survival Among Veterans Health Administration Patients: A Systematic Review and Meta-Analysis.

JAMA network open·2026
Same journal

Limbic System Microstructure in Neonates With Antenatal Opioid Exposure.

JAMA network open·2026
See all related articles

Related Experiment Video

Updated: Jun 3, 2025

Binocular Dynamic Visual Acuity in Eyeglass-Corrected Myopic Patients
07:06

Binocular Dynamic Visual Acuity in Eyeglass-Corrected Myopic Patients

Published on: March 29, 2022

2.5K

Estimating Visual Acuity With Spectacle Correction From Fundus Photos Using Artificial Intelligence.

Ashley Zhou1,2, Zhuolin Li1, William Paul3

  • 1Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, Maryland.

JAMA Network Open
|January 10, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) estimated spectacle-corrected visual acuity (VA) from fundus images in patients with diabetic macular edema (DME). The AI achieved accuracy within 1 to 1.5 lines for VA 20/80 or better, supporting its use beyond clinics.

More Related Videos

Author Spotlight: An Automated Method for Assessing Visual Acuity in Infants and Toddlers Using an Eye-Tracking System
05:10

Author Spotlight: An Automated Method for Assessing Visual Acuity in Infants and Toddlers Using an Eye-Tracking System

Published on: March 17, 2023

2.6K
Subjective Refraction Test Using a Smartphone for Vision Screening
05:36

Subjective Refraction Test Using a Smartphone for Vision Screening

Published on: October 18, 2024

601

Related Experiment Videos

Last Updated: Jun 3, 2025

Binocular Dynamic Visual Acuity in Eyeglass-Corrected Myopic Patients
07:06

Binocular Dynamic Visual Acuity in Eyeglass-Corrected Myopic Patients

Published on: March 29, 2022

2.5K
Author Spotlight: An Automated Method for Assessing Visual Acuity in Infants and Toddlers Using an Eye-Tracking System
05:10

Author Spotlight: An Automated Method for Assessing Visual Acuity in Infants and Toddlers Using an Eye-Tracking System

Published on: March 17, 2023

2.6K
Subjective Refraction Test Using a Smartphone for Vision Screening
05:36

Subjective Refraction Test Using a Smartphone for Vision Screening

Published on: October 18, 2024

601

Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Accurate spectacle-corrected visual acuity (VA) is crucial for managing ophthalmic conditions like diabetic macular edema (DME).
  • Current methods require trained technicians, increasing costs and visit times.
  • AI offers a potential solution for remote or automated VA assessment.

Purpose of the Study:

  • To evaluate the accuracy of a validated artificial intelligence (AI) algorithm in estimating spectacle-corrected visual acuity (VA) from fundus photographs in patients with diabetic macular edema (DME).
  • To assess the feasibility of using AI for VA estimation in clinical practice settings.

Main Methods:

  • Retrospective cross-sectional study of deidentified fundus photographs from 141 patients with DME.
  • Fundus images were matched with technician-measured spectacle-corrected VA from eye charts.
  • Previously validated AI algorithms were used to estimate VA from fundus images.

Main Results:

  • The AI algorithm demonstrated a mean absolute error (MAE) of 1.16 to 1.92 lines for VA between 20/10 and 20/20, and 1.42 to 1.44 lines for VA between 20/25 and 20/80.
  • Accuracy was within approximately 1 to 1.5 lines for patients with VA 20/80 or better.
  • Analysis was limited for images with VA 20/100 or worse.

Conclusions:

  • AI evaluation of fundus photographs can accurately estimate spectacle-corrected VA in patients with DME and visual acuity of 20/80 or better.
  • This technology holds promise for facilitating VA monitoring outside traditional clinical settings, potentially reducing costs and improving accessibility.
  • Further research is needed for patients with very low visual acuity.