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Related Concept Videos

Multiple Sclerosis l: Introduction01:19

Multiple Sclerosis l: Introduction

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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Personalized Longitudinal Assessment of Multiple Sclerosis Using Smartphones.

Oliver Y Chen, Florian Lipsmeier, Huy Phan

    IEEE Journal of Biomedical and Health Informatics
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    This study introduces a new smartphone-based model for personalized multiple sclerosis (MS) tracking. It uses gait, balance, and upper extremity data to predict disease progression over time.

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    Area of Science:

    • Neurology
    • Biomedical Engineering
    • Digital Health

    Background:

    • Personalized longitudinal assessment is crucial for managing multiple sclerosis (MS).
    • Identifying individual disease trajectories and profiles aids in optimal treatment adaptation.
    • Current methods may lack automated, continuous monitoring capabilities.

    Purpose of the Study:

    • To develop a novel, automated longitudinal model for assessing multiple sclerosis (MS) disease trajectories.
    • To utilize smartphone sensor data for remote, personalized MS monitoring.
    • To identify digital markers for predicting MS progression.

    Main Methods:

    • Collected smartphone sensor data on gait, balance, and upper extremity function.
    • Employed data imputation techniques for missing values.
    • Utilized generalized estimation equations to discover MS markers.
    • Ensembled parameters from multiple datasets to create a unified predictive model.
    • Incorporated subject-specific fine-tuning for improved accuracy in severe cases.

    Main Results:

    • The proposed model demonstrates promise for personalized longitudinal MS assessment.
    • Features related to gait, balance, and upper extremity function were identified as potential digital markers.
    • Remote data collection via smartphone assessments can effectively predict MS over time.
    • The model achieved accurate forecasting in previously unseen individuals with MS.

    Conclusions:

    • Smartphone-based sensor data can be leveraged for effective, personalized longitudinal monitoring of MS.
    • Gait, balance, and upper extremity function are valuable digital biomarkers for predicting MS progression.
    • The developed model offers a promising tool for automated and adaptive MS management.