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

MyoClass: A modular multimodal auto-classification system for myocardial tissue characterization.

The international journal of cardiovascular imaging·2026
Same author

Automated method for real-time AMD screening of fundus images dedicated for mobile devices.

Medical & biological engineering & computing·2022
Same author

Fast and efficient retinal blood vessel segmentation method based on deep learning network.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2021
Same author

Fully automatic brain tumor segmentation with deep learning-based selective attention using overlapping patches and multi-class weighted cross-entropy.

Medical image analysis·2020
Same author

Fully automated method for glaucoma screening using robust optic nerve head detection and unsupervised segmentation based cup-to-disc ratio computation in retinal fundus images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2019
Same author

Fully Automatic Brain Tumor Segmentation using End-To-End Incremental Deep Neural Networks in MRI images.

Computer methods and programs in biomedicine·2018
Same journal

Reduced mechanical strength correlates with decreased elastin content in aortic intima-media tissue: association with dissection in human ascending aortas.

Medical & biological engineering & computing·2026
Same journal

How plaque morphology and stenosis severity govern stent-artery interaction and deployment outcomes: a computational study.

Medical & biological engineering & computing·2026
Same journal

Investigating a relation between amyloid beta plaque burden and accumulated neurotoxicity caused by amyloid beta oligomers.

Medical & biological engineering & computing·2026
Same journal

A robot-assisted eye positioning method with high precision and repeatability for ocular particle therapy: mechanical and geometric assessment.

Medical & biological engineering & computing·2026
Same journal

Enhanced puncture event detection for teleoperated needle insertion robotic system.

Medical & biological engineering & computing·2026
Same journal

Energy-efficient real-time 4-stage sleep classification at 10-second resolution.

Medical & biological engineering & computing·2026
See all related articles

Related Experiment Video

Updated: Sep 13, 2025

Using Retinal Imaging to Study Dementia
09:17

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

21.8K

Ensemble learning-based method for multiple sclerosis screening from retinal OCT images.

Yaroub Elloumi1,2, Rostom Kachouri3

  • 1Higher Institute of Computer Science and Communication Technologies of Sousse, University of Sousse, Sousse, Tunisia. yaroub.elloumi@esiee.fr.

Medical & Biological Engineering & Computing
|August 2, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an automated method using optical coherence tomography (OCT) to detect multiple sclerosis (MS) by analyzing retinal layer thinning. The novel approach achieves high accuracy in identifying MS from OCT images.

Keywords:
DCNNEnsemble learningFeature extractionMultiple sclerosisOptical coherence tomography

More Related Videos

Author Spotlight: Ex Vivo OCT-Based Multimodal Imaging of Human Donor Eyes for Research into Age-Related Macular Degeneration
10:14

Author Spotlight: Ex Vivo OCT-Based Multimodal Imaging of Human Donor Eyes for Research into Age-Related Macular Degeneration

Published on: May 26, 2023

3.6K
Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT
12:22

Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT

Published on: August 4, 2018

8.6K

Related Experiment Videos

Last Updated: Sep 13, 2025

Using Retinal Imaging to Study Dementia
09:17

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

21.8K
Author Spotlight: Ex Vivo OCT-Based Multimodal Imaging of Human Donor Eyes for Research into Age-Related Macular Degeneration
10:14

Author Spotlight: Ex Vivo OCT-Based Multimodal Imaging of Human Donor Eyes for Research into Age-Related Macular Degeneration

Published on: May 26, 2023

3.6K
Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT
12:22

Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT

Published on: August 4, 2018

8.6K

Area of Science:

  • Ophthalmology
  • Neuroscience
  • Medical Imaging

Background:

  • Multiple sclerosis (MS) is a neurodegenerative disease affecting retinal layer thickness.
  • Optical coherence tomography (OCT) is utilized for MS diagnosis, with thinning observed in the top four retinal layers in the macula.
  • Existing MS detection methods may not fully incorporate all disease-related symptoms, potentially limiting performance.

Purpose of the Study:

  • To develop a novel automated method for detecting multiple sclerosis (MS) using retinal optical coherence tomography (OCT) images.
  • To enhance the extraction of biomarkers indicative of MS by analyzing the thickness of the four top retinal layers.
  • To improve MS detection performance by addressing limitations in existing methods.

Main Methods:

  • Employing two deep learning (DL) architectures for enhanced segmentation of OCT sub-images based on morphology.
  • Extracting thickness features from the four top retinal layers within the macular region.
  • Utilizing a dedicated classifier for each OCT cut, informed by its position relative to the macula center.
  • Implementing an ensemble learning approach to merge classifier knowledge for improved diagnostic accuracy.

Main Results:

  • The proposed method achieved high performance metrics: 97% accuracy, 100% sensitivity, 94% precision, and 94% specificity.
  • The technique successfully identified MS by analyzing retinal layer thickness in OCT scans.
  • The automated method demonstrated superior performance compared to several existing state-of-the-art approaches.

Conclusions:

  • The developed automated method effectively detects multiple sclerosis (MS) from retinal OCT images by analyzing retinal layer thickness.
  • The combination of advanced DL segmentation, feature extraction, and ensemble learning offers a robust approach for MS diagnosis.
  • This method shows significant potential for improving the early and accurate detection of MS in clinical practice.