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

Advances in Wearable Biosensors for Non-Invasive Biofluid Monitoring.

Biosensors·2026
Same author

Empowering bystanders: a psychological and institutional model for intervention in academic bullying.

Frontiers in psychology·2026
Same author

Total Hip Replacement after Acetabular Fracture Fixation: Surgical Challenges, Techniques, and Outcomes.

Journal of orthopaedic case reports·2026
Same author

Design and validation of a technology for 3D printing training phantoms for ultrasound imaging.

Physical and engineering sciences in medicine·2025
Same author

A Resource-Efficient Cardiac Arrhythmia Detection Using Nonlinear Dynamics in Optimized Delay State Networks.

IEEE transactions on bio-medical engineering·2025
Same author

A Point-of-Care Optical Biosensor for α-Amylase Estimation Using CdS/ZnS Quantum Dots.

IEEE transactions on nanobioscience·2025

Related Experiment Video

Updated: Sep 17, 2025

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
04:25

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

2.9K

Focal cortical dysplasia (type II) detection with multi-modal MRI and a deep-learning framework.

Anand Shankar1, Manob Jyoti Saikia2, Samarendra Dandapat3

  • 1Department of Electronics and Communication Engineering, Indian Institute of Information Technology Guwahati, Guwahati, Assam, 781015, India.

Npj Imaging
|July 2, 2025
PubMed
Summary

Focal cortical dysplasia type II (FCD-II), a brain malformation causing epilepsy, can be effectively analyzed using deep learning (DL) MRI techniques. The DenseNet201 model demonstrated superior performance in identifying FCD-II, aiding diagnosis and treatment planning.

More Related Videos

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
11:28

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

11.8K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.3K

Related Experiment Videos

Last Updated: Sep 17, 2025

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
04:25

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

2.9K
Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
11:28

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

11.8K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.3K

Area of Science:

  • Neuroimaging
  • Medical Artificial Intelligence
  • Developmental Neuroscience

Background:

  • Focal cortical dysplasia type II (FCD-II) is a significant brain malformation linked to drug-resistant epilepsy and cognitive deficits.
  • Magnetic Resonance Imaging (MRI) analysis is vital for FCD-II diagnosis, surgical planning, and postoperative care.
  • Deep learning (DL) offers potential for enhancing the accuracy and efficiency of FCD-II analysis.

Purpose of the Study:

  • To identify the most suitable deep learning (DL) model for analyzing Magnetic Resonance Imaging (MRI) data of Focal Cortical Dysplasia Type II (FCD-II).
  • To evaluate the performance of different DL models across various MRI modalities and image planes for FCD-II detection.

Main Methods:

  • A comprehensive study evaluated six distinct deep learning (DL) models.
  • Analysis included T1w and FLAIR MRI modalities across axial, coronal, and sagittal planes.
  • Demographic (age, sex) and clinical (hemisphere, lobes) data were incorporated into the analysis.

Main Results:

  • The DenseNet201 model exhibited superior performance in classifying FCD-II.
  • DenseNet201 achieved high precision, F1-score, and a large area under the ROC and PR curves.
  • The model's effectiveness was demonstrated across different imaging parameters and patient characteristics.

Conclusions:

  • DenseNet201 is a highly suitable DL model for the accurate analysis of FCD-II from MRI data.
  • This finding supports the integration of advanced DL techniques for improved FCD-II diagnosis and patient management.
  • Optimized DL analysis can significantly enhance presurgical planning and postoperative care for FCD-II patients.