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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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Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities.

Nida Aslam1, Irfan Ullah Khan1, Asma Bashamakh1

  • 1Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia.

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Summary

Machine learning (ML) models show promise for diagnosing Multiple Sclerosis (MS) using MRI and clinical data. Key methods like SVM, RF, and CNN offer opportunities to improve automated MS diagnosis despite current challenges.

Keywords:
artificial intelligenceclinical datadeep learningdiagnosismachine learningmagnetic resonance imaging (MRI)multiple sclerosis

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Area of Science:

  • Neurology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Multiple Sclerosis (MS) affects the central nervous system (CNS), impacting millions globally.
  • Accurate and timely diagnosis of MS is crucial for patient management and treatment.
  • Existing diagnostic methods face challenges in specificity and efficiency.

Purpose of the Study:

  • To review machine learning (ML) approaches for Multiple Sclerosis (MS) diagnosis published between 2011 and 2022.
  • To identify the most effective ML models and data types for MS diagnosis.
  • To discuss challenges and opportunities in developing advanced AI systems for MS diagnosis.

Main Methods:

  • Systematic review of ML-based MS diagnostic studies from 2011-2022.
  • Analysis of various data modalities, including magnetic resonance imaging (MRI) and clinical data.
  • Identification and comparison of prominent ML algorithms such as Support Vector Machines (SVM), Random Forests (RF), and Convolutional Neural Networks (CNN).

Main Results:

  • Several ML models have demonstrated high accuracy in diagnosing MS.
  • SVM, RF, and CNN were identified as the most frequently implemented and successful approaches.
  • The review highlights the potential of ML in enhancing the accuracy and efficiency of MS diagnosis.

Conclusions:

  • ML offers significant potential for improving automated MS diagnosis.
  • Addressing challenges like data privacy, model interpretability, and dataset size is key.
  • Future opportunities include multi-modal data integration, secure platforms, and enhanced prognosis systems for MS.