RECLAIM—A retrospective, multicenter observational study aimed at enabling the development of artificial intelligence-driven prognostic models for disease progression in multiple sclerosis

Multiple sclerosis (MS) is characterized by a progressive worsening of disability over time. As many regulatory-cleared disease-modifying treatments aiming to slow down this progression are now available, a clear need has arisen for a personalized and data-driven approach to treatment optimization in order to more efficiently slow down disease progression and eventually, progressive disability worsening. This strongly depends on the availability of biomarkers that can detect and differentiate between the different forms of disease worsening, and on predictive models to estimate the disease trajectory for each patient under certain treatment conditions. To this end, we here describe a multicenter, retrospective, observational study, aimed at setting up a harmonized database to allow the development, training, optimization, and validation of such novel biomarkers and AI-based decision models. Additionally, the data will be used to develop the tools required to better monitor this progression and to generate further insights on disease worsening and progression, patient prognosis, treatment decisions and responses, and patient profiles of patients with MS.

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Publication Details

Journal
Frontiers in Neurology
Published
2025-05-16
DOI
https://doi.org/10.3389/fneur.2025.1557947
Citations
1
Primary Topic
Multiple Sclerosis Research Studies
Type
article
Field-Weighted Citation Impact
1.47

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article

RECLAIM—A retrospective, multicenter observational study aimed at enabling the development of artificial intelligence-driven prognostic models for disease progression in multiple sclerosis

Carsten Lukas, Jelle Praet, Patrick Vermersch, Dana Horáková et al.
1 citations
Frontiers in Neurology
Multiple Sclerosis Research Studies
1.47
article

RECLAIM—A retrospective, multicenter observational study aimed at enabling the development of artificial intelligence-driven prognostic models for disease progression in multiple sclerosis

Carsten Lukas, Jelle Praet, Patrick Vermersch, Dana Horáková, Ella Maria Kadas, Koen Van Leemput, Friedemann Paul, Alexis Bernard, Lina Anderhalten, Annemie Ribbens, Noemí Gómez Manero, Cristoforo Comi, Dirk Smeets, Jean van Rampelbergh, Marjan Steppe, Tjalf Ziemssen, Erik de Boer, Vera Carina Zingler
article en
1 citations

Abstract

Multiple sclerosis (MS) is characterized by a progressive worsening of disability over time. As many regulatory-cleared disease-modifying treatments aiming to slow down this progression are now available, a clear need has arisen for a personalized and data-driven approach to treatment optimization in order to more efficiently slow down disease progression and eventually, progressive disability worsening. This strongly depends on the availability of biomarkers that can detect and differentiate between the different forms of disease worsening, and on predictive models to estimate the disease trajectory for each patient under certain treatment conditions. To this end, we here describe a multicenter, retrospective, observational study, aimed at setting up a harmonized database to allow the development, training, optimization, and validation of such novel biomarkers and AI-based decision models. Additionally, the data will be used to develop the tools required to better monitor this progression and to generate further insights on disease worsening and progression, patient prognosis, treatment decisions and responses, and patient profiles of patients with MS.

Frontiers in NeurologyVol. 16
Roche (Switzerland) (CH), Vita-Salute San Raffaele University (IT), Inserm (FR), Max Delbrück Center (DE), Charles University (CZ), Université de Lille (FR), Centre Hospitalier Universitaire de Lille (FR), Humboldt-Universität zu Berlin (DE), Massachusetts General Hospital (US), AB Science (France) (FR), Synapse (Spain) (ES), Athinoula A. Martinos Center for Biomedical Imaging (US), Bristol-Myers Squibb (Sweden) (SE), St. Josef-Hospital (DE), NeuroCognitive Institute (US), Fondation Charcot (BE), ImCyse (Belgium) (BE), General University Hospital in Prague (CZ), University Hospital Carl Gustav Carus (DE), Icometrix (Belgium) (BE), Freie Universität Berlin (DE), Technische Universität Dresden (DE), Charité - Universitätsmedizin Berlin (DE), Ruhr University Bochum (DE), Aalto University (FI)
Univerzita Karlova v Praze, ITEA, Agentschap Innoveren en Ondernemen, European Federation of Pharmaceutical Industries and Associations, European Commission, Vlaamse regering, Všeobecná Fakultní Nemocnice v Praze, HORIZON EUROPE Framework Programme
Peace, Justice and strong institutions
Openalex Percentile: Top 20%
Multiple Sclerosis Research Studies
1.47
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