Artificial intelligence applied to MRI data to tackle key challenges in multiple sclerosis.
Collorone SaraLlucia CollMarco LorenziXavier LladóJaume Sastre-GarrigaMar TintoreXavier MontalbanÀlex RoviraDeborah ParetoCarmen TurPublished in: Multiple sclerosis (Houndmills, Basingstoke, England) (2024)
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.
Keyphrases
- artificial intelligence
- big data
- multiple sclerosis
- magnetic resonance imaging
- machine learning
- contrast enhanced
- deep learning
- clinical trial
- diffusion weighted imaging
- white matter
- end stage renal disease
- clinical practice
- mass spectrometry
- ms ms
- endothelial cells
- ejection fraction
- open label
- computed tomography
- peritoneal dialysis
- public health
- newly diagnosed
- case report
- prognostic factors
- magnetic resonance
- working memory
- current status
- double blind
- patient reported outcomes
- phase iii
- phase ii
- risk assessment
- pluripotent stem cells