Automatic Active Lesion Tracking in Multiple Sclerosis Using Unsupervised Machine Learning.

ABSTRACT:

Background

Identifying active lesions in magnetic resonance imaging (MRI) is crucial for the diagnosis and treatment planning of multiple sclerosis (MS). Active lesions on MRI are identified following the administration of Gadolinium-based contrast agents (GBCAs). However, recent studies have reported that repeated administration of GBCA results in the accumulation of Gd in tissues. In addition, GBCA administration increases health care costs. Thus, reducing or eliminating GBCA administration for active lesion detection is important for improved patient safety and reduced healthcare costs. Current state-of-the-art methods for identifying active lesions in brain MRI without GBCA administration utilize data-intensive deep learning methods.

Objective

To implement nonlinear dime

SUBMITTER: Uwaeze J 

PROVIDER: S-EPMC10969435 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature