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Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...
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Radiomics and Artificial Intelligence in Multiple Sclerosis MRI: A Comprehensive Review.

Konstantinos Petrou1, Agapi Ploussi1, Ioannis Seimenis1

  • 1From the From the Department of Applied Medical Physics, Medical School (K.P., A.P., E.P.E.), 2nd Department of Radiology (G.V.), University General Hospital Attikon, National and Kapodistrian University of Athens, Rimini 1, Str, Haidari, 12462, Athens, Greece; Medical School (I.S.), National and Kapodistrian University of Athens, 75 Mikras Assias str., 11527, Athens, Greece; Medical Physics Laboratory (E.K.), Democritus University of Thrace, 69100 Alexandroupolis, Greece.

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This summary is machine-generated.

Radiomics, using AI and MRI scans, shows promise in diagnosing and predicting multiple sclerosis (MS). While effective in distinguishing lesions and predicting disability, further validation is needed for clinical use.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neurology

Background:

  • Multiple sclerosis (MS) is a chronic neurological disease.
  • Accurate diagnosis and prognosis are crucial for effective MS management.
  • Medical imaging, particularly MRI, plays a key role in MS assessment.

Purpose of the Study:

  • To review the applications of MRI radiomics in multiple sclerosis (MS) diagnosis and prognosis.
  • To synthesize findings from recent studies (2015-2025) on AI-enhanced radiomics for MS.
  • To identify the potential and limitations of radiomics in clinical MS management.

Main Methods:

  • Literature search of PubMed and Scopus databases.
  • Inclusion of studies published between 2015 and 2025.
  • Analysis of 26 selected articles on MRI radiomics and artificial intelligence in multiple sclerosis.

Main Results:

  • Radiomics features from brain MRI, combined with AI, can differentiate MS lesions from healthy tissue.
  • AI-powered radiomics shows potential in predicting MS-related disability and detecting disease activity.
  • These methods can help distinguish MS from conditions with similar symptoms.

Conclusions:

  • MRI radiomics combined with AI offers a promising approach to enhance multiple sclerosis management.
  • The technology demonstrates capabilities in diagnosis, prognosis, and disease monitoring.
  • Addressing limitations like dataset imbalance and external validation is essential for clinical integration.