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

Magnetic Resonance Imaging

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

Imaging Studies for Cardiovascular System IV: CMRI

Imaging Studies for Cardiovascular System IV: CMRI

Cardiovascular magnetic resonance imaging, or CMRI, is a non-invasive diagnostic test that employs a magnetic field and radiofrequency waves to create precise images of the heart and arteries. It provides comprehensive information about cardiac anatomy, function, perfusion, and tissue characterization without ionizing radiation.IndicationsCMRI diagnoses various heart conditions, including tissue damage from heart attacks, ischemic heart disease, myocarditis, aortic issues (tears, aneurysms,...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Integrating Participatory Social Innovation Into Requirements Engineering for AI Health Care Solutions: Case Study.

Journal of medical Internet research·2026

Same author

Toward a standardized and interoperable imaging biomarker catalog.

Insights into imaging·2026

Same author

Automated identification of MRI series using a hierarchical modular machine-learning pipeline.

European radiology experimental·2026

Same author

A pre-trained foundation model framework for multiplanar MRI classification of extramural vascular invasion and mesorectal fascia invasion in rectal cancer.

Insights into imaging·2026

Same author

Multimodal characterisation of the SMN locus in SMA: copy number quantification and hybrid gene identification.

NPJ genomic medicine·2026

Same author

The challenge of data scarcity and imbalanced classes in radiomics performance.

Computer methods and programs in biomedicine·2026

Same journal

Increased free interstitial fluid in the normal appearing brain in idiopathic normal pressure and post-hemorrhagic hydrocephalus despite unchanged choroid plexus size.

Neuroradiology·2026

Same journal

Application of angiotensin-converting-enzyme inhibitors in endovascular treatment of intracranial aneurysms.

Neuroradiology·2026

Same journal

Enhanced detection of subtle cortical abnormalities in focal epilepsy using 7 T MRI surface-based models and graph neural networks.

Neuroradiology·2026

Same journal

Anterior cerebral artery variants and their influence on endovascular outcomes: a propensity score matched analysis from the CRETA registry.

Neuroradiology·2026

Same journal

Super-resolution deep learning reconstruction for brain fluid-attenuated inversion recovery: image quality and white matter hyperintensity volume analysis.

Neuroradiology·2026

Same journal

Deep learning for contrast-enhanced MRI in pediatric brain imaging.

Neuroradiology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 6, 2026

Clinical Testing and Spinal Cord Removal in a Mouse Model for Amyotrophic Lateral Sclerosis ALS

Clinical Testing and Spinal Cord Removal in a Mouse Model for Amyotrophic Lateral Sclerosis ALS

Published on: March 17, 2012

Machine learning diagnostic model for amyotrophic lateral sclerosis analysis using MRI-derived features.

Pablo Gil Chong¹, Miguel Mazon², Leonor Cerdá-Alberich³

¹Department of Applied Statistics, Operations Research and Quality, Universitat Politècnica de València, Valencia, Spain.

|August 8, 2025

Summary

This summary is machine-generated.

Machine learning models using MRI data show promise for diagnosing Amyotrophic Lateral Sclerosis (ALS). These models analyze brain imaging features to aid in the early and accurate detection of this motor neuron disease.

More Related Videos

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

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

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

Published on: December 15, 2023

Related Experiment Videos

Last Updated: May 6, 2026

Clinical Testing and Spinal Cord Removal in a Mouse Model for Amyotrophic Lateral Sclerosis ALS

Clinical Testing and Spinal Cord Removal in a Mouse Model for Amyotrophic Lateral Sclerosis ALS

Published on: March 17, 2012

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

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

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

Published on: December 15, 2023

Area of Science:

Neuroscience
Medical Imaging
Machine Learning

Background:

Amyotrophic Lateral Sclerosis (ALS) is a progressive motor neuron disease with significant diagnostic challenges.
Current diagnostic methods for ALS lack reliable imaging biomarkers, leading to delays and difficulties in patient identification.
The development of objective diagnostic tools is crucial for timely intervention and patient management in ALS.

Purpose of the Study:

To apply machine learning (ML) algorithms to MRI-derived imaging variables for the development of diagnostic models for ALS.
To create models that can facilitate and shorten the diagnostic process for Amyotrophic Lateral Sclerosis.
To explore the utility of volumetric, cortical thickness, and local iron content data from MRI in ALS diagnosis.

Main Methods:

A dataset of 211 patients (including ALS, mimics, genetic carriers, and controls) underwent MRI scans.
Features extracted included volumetry, cortical thickness, and local iron content (T2* mapping, susceptibility imaging).
A sequential modeling approach utilized feature filtering, dimensionality reduction (PCA, kernel PCA), oversampling (SMOTE, ADASYN), and various classification techniques (logistic regression, LASSO, Random Forest, etc.).

Main Results:

The best performing model was a voting classifier using all available data, achieving an accuracy of 0.896.
This model demonstrated strong discriminative power with an Area Under the Curve (AUC) of 0.929.
High sensitivity (0.886) and specificity (0.929) were achieved, indicating robust classification performance.

Conclusions:

Machine learning techniques applied to MRI-derived imaging variables show significant potential for ALS diagnosis.
The developed diagnostic models can serve as valuable clinical tools, supporting decision-making processes.
Volumetric, cortical thickness, and local iron imaging features are promising indicators for ALS detection.