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A future of AI-driven personalized care for people with multiple sclerosis

J. Praet, L. Anderhalten, G. Comi, D. Horakova, T. Ziemssen, P. Vermersch, C. Lukas, K. van Leemput, M. Steppe, C. Aguilera, EM. Kadas, A. Bertrand, J. van Rampelbergh, E. de Boer, V. Zingler, D. Smeets, A. Ribbens, F. Paul

. 2024 ; 15 (-) : 1446748. [pub] 20240819

Language English Country Switzerland

Document type Journal Article

Persistent link   https://www.medvik.cz/link/bmc24019308

Multiple sclerosis (MS) is a devastating immune-mediated disorder of the central nervous system resulting in progressive disability accumulation. As there is no cure available yet for MS, the primary therapeutic objective is to reduce relapses and to slow down disability progression as early as possible during the disease to maintain and/or improve health-related quality of life. However, optimizing treatment for people with MS (pwMS) is complex and challenging due to the many factors involved and in particular, the high degree of clinical and sub-clinical heterogeneity in disease progression among pwMS. In this paper, we discuss these many different challenges complicating treatment optimization for pwMS as well as how a shift towards a more pro-active, data-driven and personalized medicine approach could potentially improve patient outcomes for pwMS. We describe how the 'Clinical Impact through AI-assisted MS Care' (CLAIMS) project serves as a recent example of how to realize such a shift towards personalized treatment optimization for pwMS through the development of a platform that offers a holistic view of all relevant patient data and biomarkers, and then using this data to enable AI-supported prognostic modelling.

AB Science Clinical Development Paris France

Athinoula A Martinos Center Department of Radiology Massachusetts General Hospital Charlestown MA United States

Bristol Myers Squibb Company Corp Princeton NJ United States

Center of Clinical Neuroscience Department of Neurology University Clinic Carl Gustav Carus TU Dresden Dresden Germany

Department of Computer Science Aalto University Espoo Finland

Department of Neurology and Center of Clinical Neuroscience 1st Faculty of Medicine Charles University and General University Hospital Prague Czechia

Department of Neurology Vita Salute San Raffaele University Ospedale San Raffaele Milan Italy

Department of Neurology with Experimental Neurology Charité Universitätsmedizin Berlin Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin Berlin Germany

Department of Neurorehabilitative Sciences Casa di Cura Igea Italy

Department of Neuroscience and Biomedical Engineering Aalto University Espoo Finland

European Charcot Foundation Brussels Belgium

Experimental and Clinical Research Center A Cooperation Between the Max Delbrück Center for Molecular Medicine in the Helmholtz Association and Charité Universitätsmedizin Berlin Berlin Germany

Experimental and Clinical Research Center Charité Universitätsmedizin Berlin Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin Berlin Germany

F Hoffmann La Roche Ltd Product Development Medical Affairs Neuroscience Basel Switzerland

icometrix NV Leuven Belgium

Imcyse SA Liège Belgium

Institute of Neuroradiology St Josef Hospital Ruhr University Bochum Bochum Germany

Max Delbrück Center for Molecular Medicine in the Helmholtz Association Berlin Germany

Neuroscience Clinical Research Center Charité Universitätsmedizin Berlin Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin Berlin Germany

Nocturne GmbH Berlin Germany

SYNAPSE Research Management Partners Madrid Spain

Univ Lille InsermU1172 LilNCog CHU Lille FHU Precise Lille France

References provided by Crossref.org

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$a Multiple sclerosis (MS) is a devastating immune-mediated disorder of the central nervous system resulting in progressive disability accumulation. As there is no cure available yet for MS, the primary therapeutic objective is to reduce relapses and to slow down disability progression as early as possible during the disease to maintain and/or improve health-related quality of life. However, optimizing treatment for people with MS (pwMS) is complex and challenging due to the many factors involved and in particular, the high degree of clinical and sub-clinical heterogeneity in disease progression among pwMS. In this paper, we discuss these many different challenges complicating treatment optimization for pwMS as well as how a shift towards a more pro-active, data-driven and personalized medicine approach could potentially improve patient outcomes for pwMS. We describe how the 'Clinical Impact through AI-assisted MS Care' (CLAIMS) project serves as a recent example of how to realize such a shift towards personalized treatment optimization for pwMS through the development of a platform that offers a holistic view of all relevant patient data and biomarkers, and then using this data to enable AI-supported prognostic modelling.
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