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 Experiment Video

Updated: May 13, 2026

Using Retinal Imaging to Study Dementia
09:17

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

Using deep learning to identify inherited retinal diseases based on wide-field retinal imaging data.

Leo Joskowicz1, Tim Buchbinder1, Eldan Chodorov1

  • 1School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel.

Plos One
|May 11, 2026
PubMed
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

In Vivo Assessment of Placental Structure and Perfusion in Late-Gestation Pregnancies and Their Association With Fetal Growth.

NMR in biomedicine·2026
Same author

Regional differences in fetal fat accretion in small-for-gestational-age fetuses assessed by quantitative magnetic resonance imaging.

Pediatric radiology·2026
Same author

Contextualizing Evaluation in Research Consortia: A Reflective Case Study from the Research Centers in Minority Institutions (RCMIs) Program.

International journal of environmental research and public health·2026
Same author

PROSTACYCLIN ANALOGS AND DIABETIC RETINOPATHY OUTCOMES IN PATIENTS WITH PULMONARY HYPERTENSION : A Cohort Analysis.

Retina (Philadelphia, Pa.)·2026
Same author

In-vivo MRI-based assessment of placental morphology in growth-restricted fetuses.

Placenta·2026
Same author

The PBAF chromatin remodeling complex contributes to metal homeostasis through MTF1 regulation.

Metallomics : integrated biometal science·2026

A novel deep learning method accurately identifies inherited retinal diseases (IRDs) using fundus autofluorescence (FAF) and pseudocolor fundus (pCF) images. This AI approach aids in early diagnosis and gene group prediction, potentially guiding genetic testing.

Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Inherited retinal diseases (IRDs) are a diverse group of genetic disorders affecting vision.
  • Accurate and timely diagnosis of IRDs is crucial for patient management and genetic counseling.
  • Current diagnostic methods often rely on genetic testing, which can be time-consuming and costly.

Purpose of the Study:

  • To evaluate a novel deep learning (DL) method for automated IRD identification using multimodal fundus imaging.
  • To assess the feasibility of predicting causative gene groups based on fundus autofluorescence (FAF) and pseudocolor fundus (pCF) images.
  • To explore the potential of DL in providing decision support for clinicians prior to genetic testing.

Main Methods:

  • A retrospective dataset of 409 patients with and without IRD was analyzed.

Related Experiment Videos

Last Updated: May 13, 2026

Using Retinal Imaging to Study Dementia
09:17

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

  • Wide-field FAF and pCF images, along with genetic test results, were utilized.
  • Nine EfficientNet-V2-m convolutional neural networks were trained for binary IRD classification and classification into two causative gene groups.

    • Main Results:

    • Multimodal DL models using both FAF and pCF images achieved the highest performance.
    • The binary IRD vs. non-IRD classification model demonstrated a mean accuracy of 0.95 ± 0.01.
    • Gene group classification models achieved mean accuracies of 0.92 ± 0.03 (Group 1) and 0.85 ± 0.03 (Group 2).

    Conclusions:

    • Image-based deep learning classifiers can accurately identify IRD from FAF and pCF images.
    • This AI approach offers potential decision support for clinicians during initial patient visits.
    • Further prospective validation is necessary before widespread clinical implementation.