Assessing the effectiveness of machine learning and deep learning in differentiating neuroimmunological diseases: a systematic review and meta-analysis
New AI tools, primarily utilizing MRI data, are emerging and demonstrate the potential to differentiate between various neuroimmunological disorders. While most neuroimmunological conditions have accessible antibody tests with strong diagnostic performance, AI efforts should concentrate on seronegative diseases. This approach should incorporate clinical and epidemiological data into diagnostic algorithms for improved accuracy.
Authors
- Gintaras Kaubrys (ORCID: https://orcid.org/0000-0003-4998-8403)
- Rasa Kizlaitienė (ORCID: https://orcid.org/0000-0002-0493-9686)
- Mantas Vaišvilas (ORCID: https://orcid.org/0000-0002-9306-610X)
- David Petrosian (ORCID: https://orcid.org/0009-0008-8075-9514)
- Nataša Giedraitienė (ORCID: https://orcid.org/0000-0003-2111-363X)
- Dalius Jatuzis
Institutions
- Vilnius University (LT)
Publication Details
- Journal
- Frontiers in Neurology
- Published
- 2026-01-12
- DOI
- https://doi.org/10.3389/fneur.2025.1579206
- Primary Topic
- Multiple Sclerosis Research Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00