Metadata information and fundus image fusion neural network for hyperuricemia classification in diabetes
There is a potential deep learning algorithm using fundus photographs as a noninvasively screening adjunct for hyperuricemia among individuals with diabetes. Meanwhile, combining patient's metadata enables higher screening accuracy. After applying the visualization tool, it found that the deep learning network for the identification of hyperuricemia mainly focuses on the fundus optic disc region.
Authors
- Tian Niu (ORCID: https://orcid.org/0000-0002-0377-3151)
- Su Li (ORCID: https://orcid.org/0000-0002-4132-9086)
- Hanying Wang (ORCID: https://orcid.org/0000-0002-9216-9342)
- Kun Liu (ORCID: https://orcid.org/0000-0003-0542-1739)
- Xun William Xu (ORCID: https://orcid.org/0000-0001-6294-8153)
- Yufan Wang (ORCID: https://orcid.org/0000-0001-8651-996X)
- Yan Jiang (ORCID: https://orcid.org/0000-0002-3317-1696)
- Yupeng Xu (ORCID: https://orcid.org/0000-0002-4788-7003)
- Yinchen Shen (ORCID: https://orcid.org/0000-0002-1214-711X)
- Lei Bi (ORCID: https://orcid.org/0000-0002-1972-1778)
- Jin Wei
- Tianyu Dou
- Yige Peng
Institutions
- Shanghai Jiao Tong University (CN)
- Shanghai Eye Disease Prevention & Treatment Center (CN)
Publication Details
- Journal
- Computer Methods and Programs in Biomedicine
- Published
- 2024-08-23
- DOI
- https://doi.org/10.1016/j.cmpb.2024.108382
- Citations
- 4
- Primary Topic
- Diabetic Foot Ulcer Assessment and Management
- Type
- article
- Field-Weighted Citation Impact
- 1.43
Funders
- National Natural Science Foundation of China