Integrative Machine Learning Approaches for Differentiating Pediatric Multiple Sclerosis from Non-Inflammatory Disorders via Optical Coherence Tomography - Top-Institutions - MDSpire

Integrative Machine Learning Approaches for Differentiating Pediatric Multiple Sclerosis from Non-Inflammatory Disorders via Optical Coherence Tomography

  • By

  • Chaojun Chen

  • Sahar Soltanieh

  • Sajith Rajapaksa

  • Farzad Khalvati

  • E. Ann Yeh

  • April 21, 2026

  • 0 min

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Top Institutions in Neurology

Brief introduction explaining scope and methodology.

  • #1

    Hospital for Sick Children (SickKids)
    Hospital for Sick Children (SickKids)

    Toronto, Ontario

    Key Differentiators

    • Pediatric Neurology
    • Neuroimmunology
    • Neuroimaging
    • Machine Learning in Medicine

  • #2

    Johns Hopkins University School of Medicine
    Johns Hopkins University School of Medicine

    Baltimore, Maryland

    Key Differentiators

    • Neurology
    • Neuroimmunology
    • Multiple Sclerosis Research
    • Neuroimaging

  • #3

    Mayo Clinic
    Mayo Clinic

    Rochester, Minnesota

    Key Differentiators

    • Neurology
    • Neuroimmunology
    • Ophthalmology
    • Machine Learning Applications in Neurology

  • #4

    University of California, San Francisco (UCSF)
    University of California, San Francisco (UCSF)

    San Francisco, California

    Key Differentiators

    • Neurology
    • Neuroimmunology
    • Neuroimaging
    • Biomedical Informatics

Original Source(s)

Integrative Machine Learning Approaches for Differentiating Pediatric Multiple Sclerosis from Non-Inflammatory Disorders via Optical Coherence Tomography

  • by Chaojun Chen, Sahar Soltanieh, Sajith Rajapaksa, Farzad Khalvati, E. Ann Yeh

  • April 21, 2026

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