Project title: Computer assisted morphometry of pathological changes in renal biopsies from patients with AAV
Rationale:Application of machine learning to kidney biopsy tissue morphometry, with correlation to clinical outcome parameters, can improve diagnostic and prognostic fidelity in renal AAV.
Objectives:All relating to Objective 9: Machine learning: standardised automated identification of defined changes (descriptors) in renal biopsies. Validation of machine learning: comparison of “computer” recognised and manually determined changes. First clinical outcome study: defined descriptors suited to automated analysis will be correlated to clinical outcome in a small set of patients from the MUW cohort. Clinical validation study: additional patient biopsies from RKD biobank will be included to validate automated descriptor evaluation and their suitability to predict outcome using variable clinical data sets.
Expected Results:Development of algorithms that allow automated analysis of predictive changes in renal biopsies in AAV (M3.1 (mth 36))
Planned Secondments:MUW: 5 mths (learn about disease context and coordinate tissue sections and clinical data)
Rupert Ecker: http://www.tissuegnostics.com/en/about/vision-mission