Artificial intelligence applied to MRI data to tackle key challenges in multiple sclerosis

Sara Collorone, Llucia Coll, Marco Lorenzi, Xavier Lladó, Jaume Sastre-Garriga, Mar Tintoré, Xavier Montalban, Àlex Rovira, Deborah Pareto, Carmen Tur

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Artificial intelligence (AI) is the branch of science aiming at creating algorithms able to carry out tasks that typically require human intelligence. In medicine, there has been a tremendous increase in AI applications thanks to increasingly powerful computers and the emergence of big data repositories. Multiple sclerosis (MS) is a chronic autoimmune condition affecting the central nervous system with a complex pathogenesis, a challenging diagnostic process strongly relying on magnetic resonance imaging (MRI) and a high and largely unexplained variability across patients. Therefore, AI applications in MS have the great potential of helping us better support the diagnosis, find markers for prognosis to eventually design more powerful randomised clinical trials and improve patient management in clinical practice and eventually understand the mechanisms of the disease. This topical review aims to summarise the recent advances in AI applied to MRI data in MS to illustrate its achievements, limitations and future directions.

Original languageEnglish
Pages (from-to)767-784
Number of pages18
JournalMultiple sclerosis (Houndmills, Basingstoke, England)
Volume30
Issue number7
Early online date13 May 2024
DOIs
Publication statusPublished - Jun 2024

Keywords

  • Artificial intelligence
  • Convolutional neural networks
  • Deep learning
  • Mri
  • Multiple sclerosis
  • Progressive

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