DME-RWKV: An Interpretable Multimodal Deep Learning Framework for Predicting Anti-VEGF Response in Diabetic Macular Edema
Yan Liu, Xieyang Xu, Jiaying Zhang, Hui Wang, Ao Shen, Xuefei Song, Xiaofang Xu, Yao Fu · 2025
Read DME-RWKV: An Interpretable Multimodal Deep Learning Framework for Predicting Anti-VEGF Response in Diabetic Macular Edema by Yan Liu, Xieyang Xu, Jiaying Zhang, Hui Wang, Ao Shen, Xuefei Song, Xiaofang Xu, Yao Fu (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12837810.txt.
Description
Yan Liu and co-authors teach an AI to forecast which diabetes patients will answer eye treatment. Their 2025 study targets diabetic macular edema, a swelling of the retina that steals sight, and the injected drugs that block a growth factor to dry it out.
Their model reads two kinds of eye imaging together, four hundred two eyes in all, and learns with an attention method shaped to mimic clinical reasoning. It outlined disease markers with solid accuracy and predicted treatment response at an area under the curve of about eighty-four percent, beating rival methods. The design choices also make its reasoning easier to inspect. The authors offer it as a step toward precise, explainable treatment choices in eye clinics.
Catalog record
| CREATOR | Yan Liu, Xieyang Xu, Jiaying Zhang, Hui Wang, Ao Shen, Xuefei Song, Xiaofang Xu, Yao Fu |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 44 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |