A machine learning model for predicting oligoclonal band positivity using routine cerebrospinal fluid and serum biochemical markers
A novel machine learning ensemble model accurately predicts OCB positivity using routine laboratory data and demonstrates superior performance compared with the IgG index. This approach represents a significant step in applying artificial intelligence in laboratory medicine, with the potential to enhance diagnostic efficiency. Prospective, multicenter validation is essential for broader clinical implementation.
Authors
- Pınar Akan (ORCID: https://orcid.org/0000-0001-9211-1944)
- Hazar Gözgöz (ORCID: https://orcid.org/0000-0001-8388-8807)
- Başak Akan Konuk (ORCID: https://orcid.org/0000-0003-3334-1998)
- Oğuzhan Orhan (ORCID: https://orcid.org/0009-0004-0018-1507)
Institutions
- Izmir Institute of Technology (TR)
- Dokuz Eylül University (TR)
- Technical University of Munich (DE)
Publication Details
- Journal
- American Journal of Clinical Pathology
- Published
- 2025-10-03
- DOI
- https://doi.org/10.1093/ajcp/aqaf119
- Primary Topic
- Clinical Laboratory Practices and Quality Control
- Type
- article
- Field-Weighted Citation Impact
- 0.00