Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

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

Immuno-Proteomic Features Associated to Relapse Risk in Myelin Oligodendrocyte Glycoprotein Antibody-Associated Disease.

Neurology(R) neuroimmunology & neuroinflammation·2026

Same author

Simultaneous T<sub>2</sub>, T<sub>2</sub>*, and R<sub>2</sub>' Mapping for Multiple Sclerosis Using Nonlinear Model-Based Reconstruction of Undersampled Radial RARE-EPI MRI.

Magnetic resonance in medicine·2026

Same author

Brain regional susceptibility to tauopathy in individuals at risk for chronic traumatic encephalopathy.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026

Same author

Dose-dependent white matter changes associated with repetitive head impacts in former American football players.

Brain communications·2026

Same author

Cognitive, biomarker, and neuroimaging indices associated with traumatic encephalopathy syndrome across two independent athlete cohorts.

Alzheimer's research & therapy·2026

Same author

Types and frequencies of adverse events across clinical trials for patients with amyotrophic lateral sclerosis: an analysis of the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) database.

Amyotrophic lateral sclerosis & frontotemporal degeneration·2026

Same journal

Effects of Add-On Icosapent Ethyl With Standard Treatment on Functional Outcomes and Inflammatory Biomarkers in Acute Ischemic Stroke: A Blinded Randomized Controlled Trial.

Annals of clinical and translational neurology·2026

Same journal

Baseline Neuroinflammation Stratifies TSPO-PET Response to Disease-Modifying Therapy in Multiple Sclerosis.

Annals of clinical and translational neurology·2026

Same journal

A 57-Year-Old Male With Behavioral Variant Frontotemporal Dementia and MATR3 and NOS3 Mutations.

Annals of clinical and translational neurology·2026

Same journal

Reply to: A Lethal Progressive Neuroinflammation Disguised as MOGAD Revealing a Final Diagnosis of Griscelli Syndrome: Regarding: MOGAD is the Most Common Cause of Isolated Optic Neuritis in Children.

Annals of clinical and translational neurology·2026

Same journal

A Lethal Progressive Neuroinflammation Disguised as MOGAD Revealing a Final Diagnosis of Griscelli Syndrome.

Annals of clinical and translational neurology·2026

Same journal

Long-Term Neurologic Exam Findings in People Diagnosed and Treated During Acute HIV Infection.

Annals of clinical and translational neurology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Aug 24, 2025

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential

Published on: May 25, 2020

Prior optic neuritis detection on peripapillary ring scans using deep learning.

Seyedamirhosein Motamedi¹, Sunil Kumar Yadav^1,2, Rachel C Kenney^3,4

¹Experimental and Clinical Research Center, a cooperation between the Max Delbrück Center for Molecular Medicine in the Helmholtz Association and Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.

Annals of Clinical and Translational Neurology

|October 26, 2022

Summary

This summary is machine-generated.

Deep learning models can effectively identify prior optic neuritis (ON) in multiple sclerosis (MS) patients using retinal scans. This AI approach shows promise in improving the diagnosis of demyelinating events.

More Related Videos

Using Retinal Imaging to Study Dementia

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

Induction of Paralysis and Visual System Injury in Mice by T Cells Specific for Neuromyelitis Optica Autoantigen Aquaporin-4

Induction of Paralysis and Visual System Injury in Mice by T Cells Specific for Neuromyelitis Optica Autoantigen Aquaporin-4

Published on: August 21, 2017

Related Experiment Videos

Last Updated: Aug 24, 2025

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential

Published on: May 25, 2020

Using Retinal Imaging to Study Dementia

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

Induction of Paralysis and Visual System Injury in Mice by T Cells Specific for Neuromyelitis Optica Autoantigen Aquaporin-4

Induction of Paralysis and Visual System Injury in Mice by T Cells Specific for Neuromyelitis Optica Autoantigen Aquaporin-4

Published on: August 21, 2017

Area of Science:

Ophthalmology
Neurology
Artificial Intelligence

Background:

Multiple sclerosis diagnosis requires evidence of demyelination disseminated in time and space.
Peripapillary retinal nerve fiber layer (pRNFL) thickness via optical coherence tomography (OCT) may indicate prior acute optic neuritis (ON), a common MS symptom.

Purpose of the Study:

To evaluate a deep learning (DL) network's ability to differentiate eyes with a history of ON from healthy control (HC) eyes.
To compare the DL network's performance against pRNFL thickness measurements.

Main Methods:

A dilated residual convolutional neural network was trained and validated on 1033 OCT scans from HC eyes and 510 scans from eyes with prior ON.
An independent dataset from a second center was used for external validation.
Performance was assessed using receiver operating characteristic curve analyses and area under the curve (AUC).

Main Results:

The DL network achieved an AUC of 0.86 in recognizing ON eyes, outperforming pRNFL thickness (AUC 0.77).
On the independent dataset, the DL network achieved an AUC of 0.90, compared to pRNFL's AUC of 0.84.

Conclusions:

Deep learning-based classification of prior ON is feasible.
DL models have the potential to surpass traditional thickness-based methods for classifying eyes with and without a history of ON.