Rapid and accurate recognition of erythrocytic stage parasites of Plasmodium falciparum via a deep learning-based YOLOv3 platform
The developed AI-assisted diagnostic tool exhibits robust efficiency and accuracy in <i>Plasmodium falciparum</i> recognition in clinical thin blood smears. It provides a feasible technical support for malaria control in resource-limited settings.
Authors
- Kai Wu (ORCID: https://orcid.org/0000-0002-8458-0000)
- Wei He (ORCID: https://orcid.org/0000-0002-3671-8650)
- Li Xie (ORCID: https://orcid.org/0000-0002-5555-5762)
- Jian Li (ORCID: https://orcid.org/0000-0001-9434-7803)
- Dandan Zhu (ORCID: https://orcid.org/0000-0002-6769-0996)
- Huaxu Zhu (ORCID: https://orcid.org/0000-0002-4933-6301)
- Hua Yang (ORCID: https://orcid.org/0000-0002-0153-582X)
- J. Geng (ORCID: https://orcid.org/0000-0002-1742-9976)
Institutions
- Jiangnan University (CN)
- Hubei University of Medicine (CN)
- Wuhan Center for Disease Control and Prevention (CN)
Publication Details
- Journal
- Frontiers in Microbiology
- Published
- 2025-10-30
- DOI
- https://doi.org/10.3389/fmicb.2025.1471436
- Primary Topic
- Digital Imaging for Blood Diseases
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Hubei University of Medicine
- Hubei University
- Centers for Disease Control and Prevention