TY - JOUR
T1 - Artificial intelligence applied to MRI data to tackle key challenges in multiple sclerosis
AU - Collorone, Sara
AU - Coll, Llucia
AU - Lorenzi, Marco
AU - Lladó, Xavier
AU - Sastre-Garriga, Jaume
AU - Tintoré, Mar
AU - Montalban, Xavier
AU - Rovira, Àlex
AU - Pareto, Deborah
AU - Tur, Carmen
N1 - Publisher Copyright:
© The Author(s), 2024.
PY - 2024/6
Y1 - 2024/6
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Convolutional neural networks
KW - Deep learning
KW - Mri
KW - Multiple sclerosis
KW - Progressive
UR - http://www.scopus.com/inward/record.url?scp=85194591657&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/af151eb0-2798-3dc3-b83d-58178b850708/
U2 - 10.1177/13524585241249422
DO - 10.1177/13524585241249422
M3 - Article
C2 - 38738527
SN - 1352-4585
VL - 30
SP - 767
EP - 784
JO - Multiple sclerosis (Houndmills, Basingstoke, England)
JF - Multiple sclerosis (Houndmills, Basingstoke, England)
IS - 7
ER -