Utilizing Deep Learning to Identify Electron-Dense Deposits in Renal Biopsy Electron Microscopy Images
We successfully developed a web platform for the automated assessment of the locations of electron-dense deposits in kidney biopsy EM images. The performance of this model surpasses that of experienced comprehensive renal pathologists, offering an efficient and reliable tool.
Authors
- Xiaomeng Xu (ORCID: https://orcid.org/0000-0002-1717-538X)
- Shu-zhen Lai
- Chao Yang (ORCID: https://orcid.org/0000-0001-9962-5008)
- Shuangshuang Zhu (ORCID: https://orcid.org/0000-0002-1555-1310)
- Shuling Yue
- Hongmei Yu (ORCID: https://orcid.org/0000-0002-9852-2485)
- Lei Zheng (ORCID: https://orcid.org/0000-0003-3475-118X)
- Yanqiu Liu (ORCID: https://orcid.org/0000-0002-1905-7168)
- Guang Yang (ORCID: https://orcid.org/0000-0001-5855-2113)
- An Chen (ORCID: https://orcid.org/0000-0001-7607-977X)
- Hongyu Liu (ORCID: https://orcid.org/0000-0002-8111-3941)
- Zhen Song
- Bei Luo
- Y. Gui (ORCID: https://orcid.org/0009-0004-5395-442X)
Institutions
- Kingmed Diagnostics (CN)
- Southern Medical University (CN)
Publication Details
- Journal
- American Journal of Nephrology
- Published
- 2025-05-19
- DOI
- https://doi.org/10.1159/000546131
- Primary Topic
- Renal cell carcinoma treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00