Automated Von Willebrand Factor Multimer Image Analysis for Improved Diagnosis and Classification of Von Willebrand Disease
Automated VWF multimer analysis using deep learning demonstrates high accuracy in pattern classification and could standardize the interpretation of VWF multimer patterns. While not replacing expert analysis, this approach could improve the efficiency of expert human review, potentially streamlining laboratory workflow and expanding access to VWF multimer testing.
Authors
- Jansen N. Seheult (ORCID: https://orcid.org/0000-0002-6850-7495)
- Katelynn J. Nelton
- Rajiv K. Pruthi (ORCID: https://orcid.org/0000-0002-6969-929X)
- V.A. Olteanu
- Juliana Perez Botero (ORCID: https://orcid.org/0000-0002-3674-4170)
- Dong Chen (ORCID: https://orcid.org/0000-0001-7627-9560)
- Chi Zhang (ORCID: https://orcid.org/0000-0002-4826-7877)
- Anand Ks
- Erin Aakre
Institutions
- Mayo Clinic (US)
- WinnMed (US)
Publication Details
- Journal
- International Journal of Laboratory Hematology
- Published
- 2025-03-02
- DOI
- https://doi.org/10.1111/ijlh.14455
- Primary Topic
- Platelet Disorders and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 0.00