Machine learning–enhanced digital microscopy for personalized assessment of red blood cell storage lesions
The proposed method utilizing machine learning techniques and microscopic blood smear analysis provides a rapid, accurate and stable approach for the personalized assessment of RBC storage lesions.
Authors
- Hailong Zhuo
- Li‐Ping Lv (ORCID: https://orcid.org/0000-0003-4054-008X)
- Jiang Deng (ORCID: https://orcid.org/0000-0002-3122-0300)
- Y. Zhang (ORCID: https://orcid.org/0009-0006-8111-975X)
- Ning Zhao (ORCID: https://orcid.org/0000-0003-0173-3794)
- Ke Zhang (ORCID: https://orcid.org/0009-0002-9053-4278)
- Ping Ma (ORCID: https://orcid.org/0000-0002-1172-2805)
- Chaojie Wang (ORCID: https://orcid.org/0000-0002-5916-3547)
- Qun Luo
- Liang Cao (ORCID: https://orcid.org/0000-0003-0068-4273)
Institutions
- Tianjin University of Science and Technology (CN)
- Academy of Military Medical Sciences (CN)
- Chinese PLA General Hospital (CN)
Publication Details
- Journal
- Vox Sanguinis
- Published
- 2025-08-31
- DOI
- https://doi.org/10.1111/vox.70103
- Citations
- 1
- Primary Topic
- Digital Imaging for Blood Diseases
- Type
- article
- Field-Weighted Citation Impact
- 1.28