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Related Concept Videos

Multiple Sclerosis l: Introduction01:19

Multiple Sclerosis l: Introduction

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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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Alzheimer disease is a chronic, progressive, and irreversible neurodegenerative disorder and the most common cause of dementia in older adults. It leads to gradual neuronal loss, causing cognitive decline, behavioral changes, and loss of functional independence.Risk Factors and EtiologyThe disease is multifactorial. Age is the strongest risk factor, with prevalence doubling every 5 years after age 65. Genetic factors include mutations in genes such as APP, PSEN1, and PSEN2, which are associated...
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Explainable Artificial Intelligence to Predict Neurocognitive Disorder Progression in Multiple Sclerosis Using MRI

Loredana Storelli1, Damiano Mistri1, Alice Mastropasqua1,2

  • 1Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.

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Summary
This summary is machine-generated.

Artificial intelligence accurately predicts cognitive decline in multiple sclerosis (MS) using MRI and clinical data. This approach identifies key brain changes, aiding personalized assessment and monitoring for MS patients.

Keywords:
cognitive dysfunctiondeep learningexplainable artificial intelligencemagnetic resonance imagingmultiple sclerosis

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Area of Science:

  • Neurology
  • Artificial Intelligence
  • Neuroimaging

Background:

  • Cognitive impairment is prevalent in multiple sclerosis (MS), but diagnostic frameworks for Neurocognitive Disorders (NCDs) are underutilized.
  • Integrating multimodal data with artificial intelligence (AI) for predicting cognitive outcomes in MS is an underexplored area.

Purpose of the Study:

  • To characterize NCDs in MS patients.
  • To predict cognitive worsening in MS using an explainable deep learning model.
  • To integrate MRI and clinical data for cognitive outcome prediction.

Main Methods:

  • 224 MS patients and 115 healthy controls underwent MRI and clinical assessments.
  • Neuropsychological testing and cognitive reserve estimation were performed at baseline and follow-up.
  • A deep learning model was trained on multimodal data (MRI, demographics, clinical, volumetric) to predict cognitive decline.

Main Results:

  • At baseline, 15% of MS patients met criteria for Mild or Major NCD.
  • The deep learning model achieved 90% accuracy in predicting follow-up cognitive status.
  • Key predictors identified included cortical gray matter volume, age, thalamic/hippocampal volumes, T2 lesion volume, and cognitive reserve.

Conclusions:

  • A multimodal AI approach shows strong performance in predicting cognitive worsening in MS.
  • The model highlights critical brain regions and factors associated with cognitive decline.
  • This AI strategy holds potential for personalized cognitive assessment and monitoring in MS.