Artificial Intelligence-based Techniques to Characterize KIdney Microstructure on Histological ImagEs

The primary aim of this observational exploratory study will be to use fully anonymized histological images of kidney human tissue from patients with any kidney disease and normal kidney tissue to develop novel deep learning-based image processing techniques allowing to characterize kidney microstructure across different pathologies and/or disease stages. Secondly, the study will aim at validating the novel techniques against gold standard (manual) methods, when available, and at developing novel histological imaging biomarkers that could support differential diagnosis, staging of the disease, monitoring of disease progression and response to therapy, and prediction of the disease progression. Other exploratory aims will include: * The use of radiomics techniques to identify disease-specific kidney morphology patterns. * The implementation of uncertainty quantification techniques, able to increase AI explainability.