Deep learning for the prediction of acute kidney injury after coronary angiography and intervention in patients with chronic kidney disease: a model development and validation study
The explainable DNN models serve as promising tools for predicting PC-AKI in CKD patients undergoing coronary angiography and intervention, which is crucial for risk stratification and clinical descion-making.
Authors
- Xiufen Wang
- Ying Tang (ORCID: https://orcid.org/0000-0002-3793-8401)
- Anqun Chen (ORCID: https://orcid.org/0000-0003-3714-9469)
- Dhruti P. Chen (ORCID: https://orcid.org/0000-0002-6078-8392)
- Ming Zeng (ORCID: https://orcid.org/0000-0002-4294-2077)
- Shao-Bin Duan (ORCID: https://orcid.org/0000-0002-4677-5704)
- Liyu He (ORCID: https://orcid.org/0000-0002-6637-3729)
- Chengyuan Tang (ORCID: https://orcid.org/0000-0001-9522-4469)
- Yuting Liu (ORCID: https://orcid.org/0000-0002-3100-0543)
- Xiaoqin Luo (ORCID: https://orcid.org/0009-0002-3907-1087)
- Ting Wu (ORCID: https://orcid.org/0000-0003-1691-984X)
- Xi Wu
Institutions
- Central South University (CN)
- Soil and Fertilizer Institute of Hunan Province (CN)
- Second Xiangya Hospital of Central South University (CN)
Publication Details
- Journal
- Renal Failure
- Published
- 2025-03-13
- DOI
- https://doi.org/10.1080/0886022x.2025.2474206
- Citations
- 6
- Primary Topic
- Cardiac Imaging and Diagnostics
- Type
- article
- Field-Weighted Citation Impact
- 8.16
Funders
- National Natural Science Foundation of China