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

Learning patterns of HIV-1 resistance to broadly neutralizing antibodies with reduced subtype bias using multi-task learning.

PLoS computational biology·2024
Same author

Enhancing Open Modification Searches via a Combined Approach Facilitated by Ursgal.

Journal of proteome research·2021
Same author

Expression of Nav1.5 in the pathogenesis of temporal lobe epilepsy.

Cellular and molecular biology (Noisy-le-Grand, France)·2019
Same author

Circ_0075932 in adipocyte-derived exosomes induces inflammation and apoptosis in human dermal keratinocytes by directly binding with PUM2 and promoting PUM2-mediated activation of AuroraA/NF-κB pathway.

Biochemical and biophysical research communications·2019
Same author

Chondroitin sulfate-functionalized polymeric nanoparticles for colon cancer-targeted chemotherapy.

Colloids and surfaces. B, Biointerfaces·2019
Same author

Porous Polymeric Microparticles as an Oral Drug Platform for Effective Ulcerative Colitis Treatment.

Journal of pharmaceutical sciences·2019

Related Experiment Video

Updated: Sep 28, 2025

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery
09:41

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

Published on: May 20, 2016

12.4K

Artificial intelligence for medical image analysis in epilepsy.

John Sollee1, Lei Tang2, Aime Bienfait Igiraneza3

  • 1Department of Diagnostic Radiology, Rhode Island Hospital, 593 Eddy St., Providence, RI 02903, USA; Warren Alpert Medical School of Brown University, 222 Richmond St., Providence, RI 02903, USA.

Epilepsy Research
|April 1, 2022
PubMed
Summary

Artificial intelligence (AI) enhances epilepsy diagnosis and treatment by analyzing medical imaging. Future research should integrate multimodal data and ensure model generalizability for clinical use.

Keywords:
Deep learningMachine learningNeuroimagingReviewSeizure

More Related Videos

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
12:50

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

40.4K
Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy
10:23

Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy

Published on: June 23, 2023

2.2K

Related Experiment Videos

Last Updated: Sep 28, 2025

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery
09:41

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

Published on: May 20, 2016

12.4K
Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
12:50

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

40.4K
Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy
10:23

Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy

Published on: June 23, 2023

2.2K

Area of Science:

  • Neuroscience
  • Medical Imaging Analysis
  • Artificial Intelligence

Background:

  • Deep learning artificial intelligence (AI) is a leading method for medical imaging analysis.
  • AI is increasingly utilized in clinical settings for improved patient outcomes.
  • Epilepsy research is leveraging AI for enhanced diagnosis, prognosis, and treatment.

Purpose of the Study:

  • To review current AI applications in neuroimaging analysis for epilepsy.
  • To highlight challenges specific to each imaging modality and their clinical significance.
  • To critically analyze existing AI techniques and propose future research directions.

Main Methods:

  • Review of current literature on AI in epilepsy neuroimaging.
  • Analysis of challenges and clinical significance across different imaging modalities.
  • Critical evaluation of existing AI techniques.

Main Results:

  • AI, particularly deep learning, shows significant potential in analyzing neuroimaging data for epilepsy.
  • Challenges exist in applying AI to specific imaging modalities, impacting clinical utility.
  • Existing AI techniques require further development and validation.

Conclusions:

  • A multimodal approach is recommended to leverage diverse imaging strengths and mitigate weaknesses.
  • Widespread generalizability testing of AI models is crucial before clinical implementation.
  • Increased collaboration among researchers and institutions is necessary to advance AI in epilepsy care.