Big data and artificial intelligence applied to blood and CSF fluid biomarkers in multiple sclerosis

Abstract

Fluid biomarkers; Artificial intelligence; Multiple scleorsis

Biomarcadores de fluidos; Inteligencia artificial; Esclerosis múltiple

Biomarcadors de fluids; Intel·ligència artificial; Esclerosi múltiple

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.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. CT is currently being funded by the Miguel Servet contract, awarded by the Instituto de Salud Carlos III (ISCIII), Spanish Ministry of Science and Innovation (award number: CP23/00117). She has also received research support from the ISCIII through the FORTALECE grant (FORT23/00034).