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. 2024 Aug 19:15:1446748.
doi: 10.3389/fimmu.2024.1446748. eCollection 2024.

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

Jelle Praet #  1 , Lina Anderhalten #  2 , Giancarlo Comi  3   4 , Dana Horakova  5 , Tjalf Ziemssen  6 , Patrick Vermersch  7 , Carsten Lukas  8 , Koen van Leemput  9   10   11 , Marjan Steppe  12 , Cristina Aguilera  13 , Ella Maria Kadas  14 , Alexis Bertrand  15 , Jean van Rampelbergh  16 , Erik de Boer  17 , Vera Zingler  18 , Dirk Smeets  1 , Annemie Ribbens #  1 , Friedemann Paul #  2   19   20   21   22
Affiliations

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

Jelle Praet et al. Front Immunol. .

Abstract

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.

Keywords: AI; data; diagnosis; disease progression; multiple sclerosis; personalized medicine; prognosis.

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Conflict of interest statement

JP is a shareholder of icometrix NV. AR is a shareholder of icometrix NV. DS is a shareholder of icometrix NV. MS has no relevant or material financial interests that relate to the research described in this paper. However, she is employed as Operational Director of The European Charcot Foundation. TZ reports scientific advisory board and/or consulting for Biogen, Roche, Novartis, Celgene, and Merck; compensation for serving on speaker’s bureaus for Roche, Novartis, Merck, Sanofi, Celgene, and Biogen; and research support from Biogen, Novartis, Merck, and Sanofi. VZ is a shareholder of F. Hoffmann-La Roche Ltd PV reports honorarium, contributions to meeting from Biogen, Sanofi-Genzyme, Novartis, Teva, Merck, Roche, Imcyse, AB Science, Janssen, Ad Scientiam and BMS-Celgene. Research support from Novartis, Sanofi-Genzyme and Merck. EK is a shareholder of Nocturne GmbH. DH received compensation for travel, speaker honoraria and consultant fees from Biogen Idec, Novartis, Merck, Bayer, Sanofi Genzyme, Roche, and Teva, as well as support for research activities from Biogen Idec. JR is a shareholder of Imcyse SA. GC has received consulting and speaking fees from Novartis, Sanofi, Janssen, Bristol Myers Squibb, Roche and Rewind. FP provided research support to Neurosciences Clinical Research Center, German Ministry for Education and Research (BMBF), Deutsche Forschungsgemeinschaft (DFG), Einstein Foundation, Guthy Jackson Charitable Foundation, EU FP7 Framework Program, Biogen, Genzyme, Merck Serono, Novartis, Bayer, Roche, Parexel and Almirall, received honoraria for lectures, presentations, speakers from Guthy Jackson Foundation, Bayer, Biogen, Merck Serono, Sanofi Genzyme, Novartis, Viela Bio, Roche, UCB, Mitsubishi Tanabe and Celgene, in addition, received compensation for serving on a scientific advisory board of Celgene, Roche, UCB and Merck, and is an Academic Editor of PLos One and Associate Editor of Neurology® Neuroimmunology & Neuroinflammation, all unrelated to this work. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
A clinical decision support tool should be capable of visualizing the very heterogenous MS patient data, the AI-supported analysis of this data and the outcome of prognostic models using this data, enabling a data-driven discussion between the neurologist and patient to identify the best DMT for the patient.
Figure 2
Figure 2
The first iteration of the clinical decision support platform being developed in the CLAIMS project. It offers a concise overview of the most important data for making a clinical decision.
See this image and copyright information in PMC

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