SBIR-STTR Award

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.

Project Terms:
Address; base; Biopsy; Blinded; Caring; Cessation of life; Charge; Chronic; Chronic Kidney Failure; Clinical; clinical practice; cloud based; commercial application; Computational algorithm; Computer Assisted; Computer software; computerized; Computers; Cost of Illness; Data Set; deep learning; digital; Ensure; Evaluation; Freezing; Frozen Sections; Funding; glomerulosclerosis; Goals; Health Care Costs; Healthcare Systems; Human; Image; Image Analysis; Immunohistochemistry; improved; innovation; Interobserver Variability; Kidney; Kidney Diseases; Kidney Transplantation; learning network; Life; Machine Learning; malignant breast neoplasm; Malignant neoplasm of prostate; Manuals; Measures; Medicare; meetings; Microscope; Microscopic; Modeling; Online Systems; Organ; Organ Donor; Outcome; Pathologic; Pathologist; Pathology; Patient Care; Patient-Focused Outcomes; Patients; Personal Satisfaction; Phase; power analysis; predictive modeling; Process; public health relevance; Quantitative Evaluations; Reproducibility; Research Personnel; Savings; Scientist; Secure; Slide; Small Business Technology Transfer Research; software development; Speed; standard of care; technological innovation; Testing; Time; Tissues; tool; Translating; Transplantation; Transplanted tissue; Universities; Washington; whole slide imaging; Work;

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 establish our Deep Learning automated techniques as the standard for evaluating 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 interstitial fibrosis Deep Learning model, use the automated quantitation of key microscopic findings to develop an outcome-based chronic damage score that predicts graft outcome, and test the ability of the Deep Learning models to withstand variations encountered using different scanners and processing in different laboratories. The functionality of the Trusted Kidney software platform will be improved beyond the current usable product into a commercially viable solution for multiple laboratories.

Public Health Relevance Statement:
PUBLIC HEALTH RELEVANCE STATEMENT Before kidneys can be transplanted, they must be examined using a microscope to ensure the kidney is healthy enough for transplant. A limitation of microscopic examination by pathologists is the inherent human variability in quantifying the amount of scar tissue, or chronic damage, present. The result is potentially healthy organs being discarded or damaged kidneys being used inappropriately. This funding will support developing artificial intelligence tools to assist pathologists with quantifying scar tissue in donor kidneys prior to transplantation, resulting in more consistent and objective biopsy evaluations, minimizing discard of potentially healthy kidneys, and optimizing placement of kidneys for transplant.

Project Terms:
Adoption; Americas; analytical tool; Artificial Intelligence; base; Biopsy; Canada; Cessation of life; Chronic; Cicatrix; Clinical; clinical biomarkers; cloud based; cloud platform; commercial application; Computer software; Computers; Contracts; cost; Data; Databases; deep learning; Development; Ensure; Evaluation; Fast Healthcare Interoperability Resources; Fibrosis; Frozen Sections; functional improvement; Funding; glomerulosclerosis; Goals; Gold; Graft Survival; Health Care Costs; Human; image processing; imaging biomarker; improved; innovation; Interobserver Variability; interstitial; Kidney; kidney biopsy; Kidney Diseases; Kidney Transplantation; Knowledge; Laboratories; learning strategy; Letters; Life; Machine Learning; malignant breast neoplasm; Malignant neoplasm of prostate; Manuals; Measurement; Microscope; Microscopic; Midwestern United States; Modeling; Multivariate Analysis; Online Systems; Organ; Organ Donor; Organ Procurements; Outcome; Pathologist; Pathology; pathology imaging; Patient-Focused Outcomes; Patients; Performance; Personal Satisfaction; Phase; phase 1 study; predictive modeling; Process; public health relevance; renal damage; Reproducibility of Results; Research Personnel; Savings; Scanning; Scientist; Secure; Services; shared database; Slide; Small Business Technology Transfer Research; Specialist; Speed; standard of care; System; Techniques; technological innovation; Testing; Tissues; tool; Transplantation; Trichrome stain; Trichrome stain method; Trust; United Network for Organ Sharing; Universities; Variant; Washington; whole slide imaging; Work