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Multiple Sclerosis l: Introduction01:19

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Comprehensive Autopsy Program for Individuals with Multiple Sclerosis
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Deep learning-based neuroanatomical profiling reveals population-specific brain changes in multiple sclerosis: a

Mahdi Bashiri Bawil1, Mousa Shamsi2, Abolhassan Shakeri Bavil3

  • 1Biomedical Engineering Faculty, Sahand University of Technology, Tabriz, Iran.

BMC Medical Imaging
|June 2, 2026
PubMed
Summary
This summary is machine-generated.

Multiple sclerosis (MS) patients show increased brain structural changes, including higher ventricular and white matter lesion loads, compared to healthy controls. This study establishes population-specific neuroimaging biomarkers for Middle Eastern MS cohorts.

Keywords:
Deep learningFLAIR segmentationMRIMultiple sclerosis (MS)NeuroimagingPopulation-specific biomarkers

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Area of Science:

  • Neuroimaging
  • Neurology
  • Artificial Intelligence in Medicine

Background:

  • Multiple sclerosis (MS) affects millions globally, with underrepresentation of Middle Eastern populations in neuroimaging research.
  • Large-scale comparative studies are crucial for understanding MS-related brain changes and developing biomarkers.
  • This study focuses on a Middle Eastern cohort to address demographic gaps in MS research.

Purpose of the Study:

  • To perform a comprehensive statistical analysis of brain structural differences between MS patients and healthy controls (HC) in a Middle Eastern population.
  • To establish population-specific reference ranges for neuroimaging biomarkers in MS using automated deep learning segmentation.
  • To characterize lesion accumulation patterns and their correlation with age and gender.

Main Methods:

  • Retrospective analysis of 1,381 subjects (381 MS patients, 1,000 HC) from Northwest Iran.
  • Automated segmentation of FLAIR sequences using a U-Net deep learning architecture.
  • Neuroanatomically-informed classification of white matter hyperintensities and statistical comparisons stratified by age and gender.

Main Results:

  • U-Net achieved high segmentation performance (DSC=88.8%).
  • MS patients exhibited significantly higher ventricular load (1.7-fold) and white matter lesion load (2.4-fold) compared to HC (p < 10⁻⁶).
  • Periventricular lesions were predominant (53.91%), and age-related structural changes were more pronounced in MS patients.

Conclusions:

  • The study provides preliminary population-specific reference ranges for MS neuroimaging biomarkers in Middle Eastern populations.
  • Automated segmentation and statistical framework address demographic gaps in global MS research.
  • Findings reveal distinct lesion accumulation patterns, particularly periventricular predominance, in this cohort.