A Deep Learning Model to Quantify Arteriosclerosis in Donor Kidney Biopsies

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ABSTRACT More people die every year from kidney disease than breast or prostate cancer. Kidney transplantation is life-saving, yet the donor organ shortage and high organ discard rate contributes to 13 deaths daily among patients awaiting transplant. The decision to use or discard a donor kidney relies heavily on microscopic quantitation of chronic damage by pathologists. The current standard of care relies on a manual process that is subject to significant human variability and inefficiency, resulting in potentially healthy kidneys being discarded and potentially damaged kidneys being transplanted inappropriately. Our team developed the first Deep Learning model to quantify percent global glomerulosclerosis in donor kidney frozen section biopsy whole slide images. We developed a cloud-based platform to apply the Deep Learning model to analyze kidney biopsy whole slide images in under 6 minutes with accuracy and precision equal to or greater than current standard of care pathologists. We have also developed a Deep Learning model to quantify interstitial fibrosis on donor kidney biopsy whole slide images. 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, improved patient outcomes, and diminished health care costs. The goal of this project is to develop a Deep Learning technique for quantification of arteriosclerosis, to support evaluation of donor kidneys prior to transplantation. This will be achieved by assembling a team of expert pathologists and computer scientists specializing in machine learning. The proposal will evaluate the accuracy and precision of the arteriosclerosis Deep Learning model. The functionality of the Trusted Kidney software platform will be improved beyond the current usable product into a commercially viable solution for multiple laboratories.