Big data and artificial intelligence applied to blood and CSF fluid biomarkers in multiple sclerosis
Arrambide, Georgina (Hospital Universitari Vall d'Hebron. Institut de Recerca)
Comabella López, Manuel (Hospital Universitari Vall d'Hebron)
Tur, Carmen (University College London)

Data:	2024
Resum:	Artificial intelligence (AI) has meant a turning point in data analysis, allowing predictions of unseen outcomes with precedented levels of accuracy. In multiple sclerosis (MS), a chronic inflammatory-demyelinating condition of the central nervous system with a complex pathogenesis and potentially devastating consequences, AI-based models have shown promising preliminary results, especially when using neuroimaging data as model input or predictor variables. The application of AI-based methodologies to serum/blood and CSF biomarkers has been less explored, according to the literature, despite its great potential. In this review, we aimed to investigate and summarise the recent advances in AI methods applied to body fluid biomarkers in MS, highlighting the key features of the most representative studies, while illustrating their limitations and future directions.
Ajuts:	"la Caixa" Foundation ID00010434 Instituto de Salud Carlos III PI21/01860
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	multiple scleorsis (MS) ; Fluid biomarkers ; Demyelinating ; machine learning and AI ; Deep learning
Publicat a:	Frontiers in immunology, Vol. 15 (october 2024) , ISSN 1664-3224

DOI: 10.3389/fimmu.2024.1459502
PMID: 39493759

18 p, 1.6 MB

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