logo
ResearchBunny Logo
Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data

Medicine and Health

Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data

A. Eshaghi, A. L. Young, et al.

This groundbreaking study led by authors Arman Eshaghi and colleagues utilizes unsupervised machine learning on MRI scans from thousands of MS patients to reveal distinct subtypes of multiple sclerosis. The findings shed light on how these subtypes predict disability progression and treatment response, potentially transforming patient care and clinical trials.

00:00
00:00
Playback language: English
Abstract
This study uses unsupervised machine learning and brain MRI scans from 6322 MS patients (training dataset) and 3068 (validation dataset) to identify MS subtypes based on pathological features. Three subtypes were defined: cortex-led, normal-appearing white matter (NAWM)-led, and lesion-led. The lesion-led subtype showed the highest risk of disability progression and relapse rate, but also a positive treatment response in selected clinical trials. Findings suggest that MRI-based subtypes predict MS disability progression and treatment response, potentially improving patient stratification in interventional trials.
Publisher
Nature Communications
Published On
Apr 06, 2021
Authors
Arman Eshaghi, Alexandra L. Young, Peter A. Wijeratne, Ferran Prados, Douglas L. Arnold, Sridar Narayanan, Charles R. G. Guttmann, Frederik Barkhof, Daniel C. Alexander, Alan J. Thompson, Declan Chard, Olga Ciccarelli
Tags
multiple sclerosis
machine learning
MRI scans
subtypes
disability progression
treatment response
patient stratification

Related Publications

Explore these studies to deepen your understanding of the subject.

Improved Fault Classification and Localization in Power Transmission Networks Using VAE-Generated Synthetic Data and Machine Learning Algorithms
Engineering and Technology

Improved Fault Classification and Localization in Power Transmission Networks Using VAE-Generated Synthetic Data and Machine Learning Algorithms

M. A. Khan, B. Asad, et al.

Machine-learning algorithms for asthma, COPD, and lung cancer risk assessment using circulating microbial extracellular vesicle data and their application to assess dietary effects
Medicine and Health

Machine-learning algorithms for asthma, COPD, and lung cancer risk assessment using circulating microbial extracellular vesicle data and their application to assess dietary effects

A. Mcdowell, J. Kang, et al.

Identification and epidemiological characterization of Type-2 diabetes sub-population using an unsupervised machine learning approach
Medicine and Health

Identification and epidemiological characterization of Type-2 diabetes sub-population using an unsupervised machine learning approach

S. Bej, J. Sarkar, et al.

Rapid and flexible segmentation of electron microscopy data using few-shot machine learning
Engineering and Technology

Rapid and flexible segmentation of electron microscopy data using few-shot machine learning

S. Akers, E. Kautz, et al.

Listen, Learn & Level Up
Over 10,000 hours of research content in 25+ fields, available in 12+ languages.
No more digging through PDFs, just hit play and absorb the world's latest research in your language, on your time.
listen to research audio papers with researchbunny