AI-assisted identification of disability patterns within identical EDSS grades
Applying Artificial Intelligence (AI) to large data sets of high-quality clinical assessments allows for distinguishing among different subscore patterns within identical EDSS scores.
Authors
- Marcus D’Souza (ORCID: https://orcid.org/0000-0001-5175-9541)
- Manar Aoun
- Magdalena Mroczek (ORCID: https://orcid.org/0000-0002-8731-0540)
- Jens Kühle (ORCID: https://orcid.org/0000-0002-6963-8892)
- Po‐Jui Lu (ORCID: https://orcid.org/0000-0001-6031-6865)
- Ludwig Kappos (ORCID: https://orcid.org/0000-0003-4175-5509)
- Nuria Cerdá-Fuertes (ORCID: https://orcid.org/0000-0001-8753-5267)
- David Leppert (ORCID: https://orcid.org/0000-0001-6172-801X)
- Athina Papadopoulou (ORCID: https://orcid.org/0000-0002-0367-1008)
- Sophie Arnould
- Cristina Granziera (ORCID: https://orcid.org/0000-0002-4917-8761)
- Martina Greselin (ORCID: https://orcid.org/0009-0002-9398-5306)
- Anastasios Demirtzoglou
Institutions
- Novartis (Switzerland) (CH)
- University of Basel (CH)
- University Hospital of Basel (CH)
Publication Details
- Journal
- Multiple Sclerosis Journal
- Published
- 2025-04-18
- DOI
- https://doi.org/10.1177/13524585251327300
- Primary Topic
- Multiple Sclerosis Research Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00