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

改善病理学家对供体肾活检的解释的深度学习模型

基本信息

批准号：
9678574
负责人：
Joseph P Gaut
金额：
$ 21.4万
依托单位：
NEWVENTUREIQ, LLC
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-21 至 2020-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9678574
关键词：
Address Biopsy Blinded Caring Cessation of life Charge Chronic Chronic Kidney Failure Clinical Computational algorithm Computer Assisted Computer software Computers Cost of Illness Data Set Ensure Evaluation Freezing Frozen Sections Funding Goals Health Care Costs Healthcare Systems Human Image Image Analysis Immunohistochemistry Interobserver Variability Kidney Kidney Diseases Kidney Transplantation Life Machine Learning Malignant neoplasm of prostate Manuals Measures Medicare Microscope Microscopic Modeling Online Systems Organ Organ Donor Outcome Pathologic Pathologist Pathology Patient Care Patient-Focused Outcomes Patients Personal Satisfaction Phase Process Quantitative Evaluations Reproducibility Research Personnel Savings Scientist Secure Slide Small Business Technology Transfer Research Speed Testing Time Tissues Translating Transplantation Transplanted tissue Universities Washington Work base clinical practice cloud based commercial application computerized deep learning digital glomerulosclerosis improved innovation learning network malignant breast neoplasm meetings power analysis predictive modeling public health relevance software development standard of care technological innovation tool whole slide imaging

项目摘要

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.

项目总结/摘要每年死于肾脏疾病的人比死于乳腺癌或前列腺癌的人多。肾移植是挽救生命的，但受到器官捐献者短缺和不可接受的供体器官丢弃率高。决定使用或丢弃捐赠者的肾脏在很大程度上取决于病理学家对关键显微镜检查结果进行手动定量。这一点的一个主要局限是，显微镜检查是人类在解释发现时的可变性和低效率，导致潜在健康的器官被认为不适合移植或潜在地受损器官被不当移植我们的团队开发了第一个一种可自动量化肾小球硬化整体百分比的学习模型供肾冰冻切片楔形活检切片图像。这种创新的方法具有通过提高病理学家的效率来改变供体肾活检评价的潜力，准确性和精确性最终导致优化的供体器官利用，医疗保健成本和改善患者的结果。这个项目的目标是建立我们的深度学习自动化定量评估作为供体肾脏的标准实践移植前评估。这将通过组建一个肾脏专家团队来实现病理学家和专门从事机器学习的计算机科学家。该提案将评估计算机化方法的准确性和精确性，以量化全球肾小球硬化症，并将这些结果与当前的护理标准病理学家进行比较评价部署深度学习模型来分析整个幻灯片图像的可行性云计算也将受到考验。该STTR的最终产品将是一个基于网络的平台安全地部署深度学习图像分析作为一种工具，以帮助病理学家进行供体肾活检评价。