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Multiple Sclerosis l: Introduction01:19

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Multiple sclerosis is a chronic autoimmune disease of the central nervous system (CNS) that affects the brain, spinal cord, and optic nerves. It is an inflammatory demyelinating disorder and a leading cause of neurological disability in young adults.EpidemiologyMS commonly begins between 20 and 40 years of age and is twice as common in women. Its exact cause remains unclear, but genetic susceptibility contributes, with higher risk in first-degree relatives and identical twins. A greater...
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Area of Science:

  • Neurology
  • Radiology
  • Artificial Intelligence

Background:

  • Radiologically Isolated Syndrome (RIS) presents incidental MRI findings suggesting multiple sclerosis (MS) in asymptomatic individuals.
  • Predicting conversion from RIS to clinical MS is challenging, despite known risk factors like age and biomarkers.
  • Unsupervised machine learning offers potential for uncovering MRI-driven MS phenotypes and progression patterns.

Purpose of the Study:

  • To evaluate an unsupervised artificial intelligence (AI) framework using generative manifold learning for stratifying RIS patients by conversion risk.
  • To assess the utility of AI-generated digital twins from MRI data for risk stratification.
  • To identify distinct RIS patient clusters with varying five-year conversion risks.

Main Methods:

  • Studied 152 RIS individuals (32 converters), 152 MS patients, and 152 healthy controls.
  • Utilized an AI framework (BrainGML-MS) for analyzing imaging biomarkers and generating individualized digital twins from MRI data.
  • Applied unsupervised generative manifold learning to stratify RIS patients.

Main Results:

  • The AI model identified four RIS clusters with five-year conversion risks from 10% to 39%.
  • A progressive increase in brain age gap was observed from healthy controls to RIS non-converters, RIS converters, and MS patients.
  • RIS converters exhibited greater structural atrophy and resembled MS patient profiles more closely.

Conclusions:

  • MRI-derived brain aging biomarkers and structural deviations at the initial RIS scan can improve early risk stratification.
  • AI-driven analysis of MRI data shows promise for supporting clinical decision-making in preclinical MS.
  • This approach may enhance the identification of individuals at higher risk of developing clinical MS from RIS.