A machine learning model for predicting anatomical response to Anti-VEGF therapy in diabetic macular edema
We successfully developed a predictive model for anatomical response to anti-VEGF therapy in DME patients. The model identified key features associated with treatment outcomes, providing a valuable tool for personalized therapeutic planning. Further validation in multicenter cohorts is warranted to confirm generalizability and enhance model robustness.
Authors
- Wenrui Lu (ORCID: https://orcid.org/0000-0001-6328-714X)
- X.C. Wang
- Kunhong Xiao (ORCID: https://orcid.org/0009-0008-7805-7193)
- Yan Lou (ORCID: https://orcid.org/0000-0002-2248-0231)
- Yun Ye (ORCID: https://orcid.org/0000-0002-1905-4354)
- Li Li (ORCID: https://orcid.org/0000-0002-9796-8987)
- Yuqing Wang (ORCID: https://orcid.org/0000-0002-2169-8626)
- Wenbin Chen (ORCID: https://orcid.org/0000-0001-9696-8451)
- Xuemei Zhang (ORCID: https://orcid.org/0000-0003-3970-7110)
Institutions
- Fujian Medical University (CN)
- Fujian Provincial Hospital (CN)
- Fuzhou University (CN)
- China Medical University (CN)
Publication Details
- Journal
- Frontiers in Cell and Developmental Biology
- Published
- 2025-05-30
- DOI
- https://doi.org/10.3389/fcell.2025.1603958
- Citations
- 3
- Primary Topic
- Retinal Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 4.08