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

Brief summary (registry, in English)

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.

What you commit to

• Study type: Observational
• Planned participants: 100
• From 8 Nov 2024 to the primary completion date, Nov 2034
• 1 site in 1 country
• Visits and tests: ask the trial team, the registry does not list them

Built from the registry fields of ClinicalTrials.gov. It describes the study, not your situation.

Who cannot take part

Copied verbatim from ClinicalTrials.gov and grouped by topic. Only the trial team decides who can take part.

Who the study is looking for

Sex: All · Accepts healthy volunteers

Full criteria on ClinicalTrials.gov

Sites (1)

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