Efficient quality control of platelet-rich plasma preparation using computer vision and deep learning
We demonstrate a nondestructive, real-time QC method for PRP preparation with computer vision and deep learning, offering a practical and scalable solution to improve clinical outcomes in regenerative medicine.
Authors
- G. M. Liu
- Jing Hong (ORCID: https://orcid.org/0000-0002-8079-2073)
- Wei He (ORCID: https://orcid.org/0000-0002-4010-1557)
- Li Luo (ORCID: https://orcid.org/0000-0001-7975-0012)
- Wanyue Li (ORCID: https://orcid.org/0000-0003-0187-7376)
- Wangxiang Mai (ORCID: https://orcid.org/0000-0003-4121-5131)
- Rong Mo (ORCID: https://orcid.org/0000-0002-3331-7288)
- Zhuoming Chen
Institutions
- Guangdong University of Technology (CN)
- Zhaoqing University (CN)
- First Affiliated Hospital of Jinan University (CN)
- South China University of Technology (CN)
Publication Details
- Journal
- Journal of Biomedical Optics
- Published
- 2025-06-30
- DOI
- https://doi.org/10.1117/1.jbo.30.6.065003
- Citations
- 2
- Primary Topic
- Surface Roughness and Optical Measurements
- Type
- article
- Field-Weighted Citation Impact
- 2.67
Funders
- National Natural Science Foundation of China
- Guangdong Medical Research Foundation
- Basic and Applied Basic Research Foundation of Guangdong Province