Utilizing Podocyte Foot Process Morphology for the Identification of Diabetic Nephropathy with or without Minimal Change Disease: Establishment of an Artificial Intelligence-Assisted Diagnostic Model
Podocyte foot process morphology has diagnostic value in differentiating whether DN is complicated by MCD. Our AI model addresses the unmet clinical need for reliable differentiation between DN with and without concurrent MCD. Additionally, it establishes a foundational framework for AI-powered analysis of renal imaging data to improve disease diagnosis and prognosis prediction.
Authors
- Haisong Zhang (ORCID: https://orcid.org/0009-0008-0567-3673)
- Shunlai Shang (ORCID: https://orcid.org/0000-0002-6785-7717)
- Xiangmeng Li (ORCID: https://orcid.org/0000-0003-1263-5495)
- Peihua Zhang
- Chenchen Li (ORCID: https://orcid.org/0000-0003-0937-0350)
- Jinyu Liu (ORCID: https://orcid.org/0000-0002-1000-9434)
- Yan Gao (ORCID: https://orcid.org/0009-0006-6216-3165)
- Shimin Jiang (ORCID: https://orcid.org/0000-0001-5265-7395)
- Jiao Zhang
- Wenge Li
Institutions
- Beijing University of Chinese Medicine (CN)
- Capital Medical University (CN)
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- China-Japan Friendship Hospital (CN)
- Peking Union Medical College Hospital (CN)
- Affiliated Hospital of Hebei University (CN)
- Hebei Finance University (CN)
Publication Details
- Journal
- Diabetes Metabolic Syndrome and Obesity
- Published
- 2025-07-01
- DOI
- https://doi.org/10.2147/dmso.s525765
- Citations
- 3
- Primary Topic
- Systemic Sclerosis and Related Diseases
- Type
- article
- Field-Weighted Citation Impact
- 4.41
Funders
- National Natural Science Foundation of China
- China Health Promotion Foundation
- China-Japan Friendship Hospital