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

Is Current Evidence Sufficient to Establish the Efficacy of Botulinum Toxin A in Treating Persistent Dry Eye Disease? A Systematic Review and Meta-Analysis of Interventional Studies With a Critical Review Using GRADE Tool.

Health science reports·2026
Same author

Re: 'The effect of botulinum toxin A on dry eye disease and syndromes'.

Clinical & experimental optometry·2026
Same author

Botulinum toxin a for dry eye disease: impact of population mixing and statistical issues.

Eye (London, England)·2026
Same author

Memantine, an NMDA Receptor Antagonist, Attenuates Doxorubicin-Induced Cardiac Oxidative Stress and Inflammation in Mouse 4T1 Breast Cancer Model.

Breast cancer : basic and clinical research·2025
Same author

Transverse Sinus Hypoplasia as a Differential Diagnosis for Cerebral Vein Thrombosis: A Case Report.

Clinical case reports·2025
Same author

Artificial intelligence for early detection of diabetes mellitus complications via retinal imaging.

Journal of diabetes and metabolic disorders·2025

Related Experiment Video

Updated: Jul 8, 2025

Methodology for Biomimetic Chemical Neuromodulation of Rat Retinas with the Neurotransmitter Glutamate In Vitro
12:56

Methodology for Biomimetic Chemical Neuromodulation of Rat Retinas with the Neurotransmitter Glutamate In Vitro

Published on: December 19, 2017

7.8K

Artificial Intelligence for Multiple Sclerosis Management Using Retinal Images: Pearl, Peaks, and Pitfalls.

Shadi Farabi Maleki1, Milad Yousefi2, Sayeh Afshar1

  • 1Nikookari Eye Center, Tabriz University of Medical Sciences, Tabriz, Iran.

Seminars in Ophthalmology
|December 13, 2023
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) enhances Optical coherence tomography (OCT) analysis for multiple sclerosis (MS) diagnosis and progression prediction. AI algorithms improve accuracy in detecting MS retinal changes, aiding personalized treatment and patient care.

Keywords:
Artificial intelligencemultiple sclerosisneural networkretinaretinal imagingreview

More Related Videos

Using Retinal Imaging to Study Dementia
09:17

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

21.7K
Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
10:50

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

1.8K

Related Experiment Videos

Last Updated: Jul 8, 2025

Methodology for Biomimetic Chemical Neuromodulation of Rat Retinas with the Neurotransmitter Glutamate In Vitro
12:56

Methodology for Biomimetic Chemical Neuromodulation of Rat Retinas with the Neurotransmitter Glutamate In Vitro

Published on: December 19, 2017

7.8K
Using Retinal Imaging to Study Dementia
09:17

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

21.7K
Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
10:50

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

1.8K

Area of Science:

  • Neuroscience
  • Ophthalmology
  • Medical Imaging

Background:

  • Multiple sclerosis (MS) is a CNS autoimmune disease with inflammatory, demyelinating, and neurodegenerative components.
  • Retinal imaging, especially Optical coherence tomography (OCT), is vital for assessing MS-related retinal injury.
  • Artificial intelligence (AI) integration shows potential for advanced OCT analysis in MS.

Purpose of the Study:

  • To review current research on AI, including machine learning (ML) and deep learning (DL), integrated with OCT for MS.
  • To examine AI's role in MS diagnosis, disease progression monitoring, and patient care.
  • To discuss advancements, challenges, and ethical considerations of AI in MS OCT analysis.

Main Methods:

  • Review of current research studies on AI algorithms (ML/DL) applied to OCT imaging in multiple sclerosis.
  • Analysis of AI's capabilities in detecting, classifying, and segmenting MS-related retinal abnormalities.
  • Evaluation of AI's prognostic value in predicting MS disease progression using longitudinal OCT data.

Main Results:

  • AI algorithms demonstrate high accuracy in detecting and classifying MS-related retinal abnormalities on OCT scans.
  • AI enhances OCT image segmentation, streamlining diagnosis and reducing human error.
  • AI-driven prognostic models predict MS disease progression, enabling early intervention and personalized treatment.

Conclusions:

  • AI integration with OCT significantly advances MS diagnosis, monitoring, and personalized treatment planning.
  • AI tools offer improved efficiency, accuracy, and prognostic capabilities for managing MS.
  • Future directions include developing AI-powered screening tools and supporting clinical decision-making in MS care.