From fundus to filtration: AI-driven retinal phenotyping as a framework for non-invasive prediction of kidney pathological categories (the "virtual renal biopsy" concept)- a narrative review.
- DOI
- 10.1007/s11255-026-05379-8
- Published
- 2026-09-14
- Container
- Int Urol Nephrol
- Publisher
- Not recorded
- Open access
- no
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Cite this work
BibTeX
@article{allodium:10.1007/s11255-026-05379-8,
title = {From fundus to filtration: AI-driven retinal phenotyping as a framework for non-invasive prediction of kidney pathological categories (the "virtual renal biopsy" concept)- a narrative review.},
author = {Soliman AR and Guirguis KJ and Kamal NA.},
year = {2026},
journal = {Int Urol Nephrol},
doi = {10.1007/s11255-026-05379-8},
url = {https://doi.org/10.1007/s11255-026-05379-8}
}RIS
TY - JOUR TI - From fundus to filtration: AI-driven retinal phenotyping as a framework for non-invasive prediction of kidney pathological categories (the "virtual renal biopsy" concept)- a narrative review. AU - Soliman AR AU - Guirguis KJ AU - Kamal NA. PY - 2026 JO - Int Urol Nephrol DO - 10.1007/s11255-026-05379-8 UR - https://doi.org/10.1007/s11255-026-05379-8 ER -
APA
AR, S., KJ, G., & NA., K. (2026). From fundus to filtration: AI-driven retinal phenotyping as a framework for non-invasive prediction of kidney pathological categories (the "virtual renal biopsy" concept)- a narrative review.. Int Urol Nephrol. https://doi.org/10.1007/s11255-026-05379-8
Source records
- europe-pmc · retrieved 2026-09-25T19:39:35.922Z