Automated Analysis of Kidney Transplant Biopsies

Neuropathologists currently visually assess and grade kidney biopsies to reach multiple decisions related to diagnosis, prognosis, and relevant treatment approaches. This process is tedious, requires years of training, and is limited. The limitation of this method spans from problems in reproducibility and accuracy in biopsy grading, diagnosis, and prognosis. The ability to develop new and more sensitive prognosis estimations, predict kidney transplant rejections, and better understand disease progression. This project aims to develop AI technologies that can help to objectify, automate, and predict kidney transplant rejection. Ultimately, this project will aim to increase the understanding of disease clusters and their related manifestations in renal biopsies.