A Deep Learning Model to Improve Pathologist Interpretation of Donor Kidney Biopsies

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PROJECT SUMMARY/ABSTRACT More people die every year from kidney disease than breast or prostate cancer. Kidney transplantation is life-saving but is limited by a shortage of organ donors and an unacceptably high donor organ discard rate. The decision to use or discard a donor kidney relies heavily on manual quantitation of key microscopic findings by pathologists. A major limitation of this microscopic examination is human variability and inefficiency in interpreting the findings, resulting in potentially healthy organs being deemed unsuitable for transplantation or potentially damaged organs being transplanted inappropriately. Our team developed the first Deep Learning model capable of automatically quantifying percent global glomerulosclerosis in whole slide images of donor kidney frozen section wedge biopsies. This innovative approach has the potential to transform donor kidney biopsy evaluation by improving pathologist efficiency, accuracy, and precision ultimately resulting in optimized donor organ utilization, diminished health care costs, and improved patient outcomes. The goal of this project is to establish our Deep Learning automated quantitative evaluation as the standard practice of donor kidney evaluation prior to transplantation. This will be achieved by assembling a team of expert kidney pathologists and computer scientists specializing in machine learning. The proposal will evaluate the accuracy and precision of the computerized approach to quantifying percent global glomerulosclerosis and compare these results with current standard of care pathologist evaluation. The feasibility of deploying the Deep Learning model to analyze whole slide images on the cloud will also be examined. The end product of this STTR will be a web-based platform to securely deploy Deep Learning image analysis as a tool to assist pathologists with donor kidney biopsy evaluation.