Recognition of parasitic helminth eggs via a deep learning-based platform
The results show that this AI-assisted platform significantly reduces reliance on professional expertise while maintaining real-time efficiency and high accuracy, offering a powerful tool for the diagnosis and treatment of parasitosis. With further optimization, such as expanding training datasets and refining recognition algorithms, this AI system could become a key resource in both clinical and public health efforts to combat parasitic infections.
Authors
- Y. Li (ORCID: https://orcid.org/0000-0002-6171-0677)
- Haimei Shi
- Huidi Wang
- Lixin Xie (ORCID: https://orcid.org/0009-0006-8117-2067)
- Dandan Zhu (ORCID: https://orcid.org/0000-0002-6769-0996)
- Huaxu Zhu (ORCID: https://orcid.org/0000-0002-4933-6301)
- Xiao Hu (ORCID: https://orcid.org/0000-0001-9478-5571)
- Hua Yang (ORCID: https://orcid.org/0000-0002-0153-582X)
- J. Geng (ORCID: https://orcid.org/0000-0002-1742-9976)
- Y. H. Wang
- Jian Li (ORCID: https://orcid.org/0000-0002-5206-2807)
- Wei He (ORCID: https://orcid.org/0000-0002-3100-9724)
Institutions
- Jiangnan University (CN)
- Hubei University of Medicine (CN)
- Shanghai CASB Biotechnology (China) (CN)
- Taihe Hospital (CN)
Publication Details
- Journal
- Frontiers in Microbiology
- Published
- 2024-11-20
- DOI
- https://doi.org/10.3389/fmicb.2024.1485001
- Citations
- 10
- Primary Topic
- COVID-19 diagnosis using AI
- Type
- article
- Field-Weighted Citation Impact
- 4.04
Funders
- Hubei University of Medicine
- Hubei University