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.

项目摘要/摘要每年因肾脏疾病而死的人数比乳腺癌或前列腺癌多。肾移植是挽救生命的，但受到器官捐献者短缺的限制和不可接受的高供体器官丢弃率。使用或丢弃捐赠者肾脏的决定非常依赖病理学家对关键显微镜发现的手动定量。这是一个主要限制显微镜检查是人类的可变性和效率低下，解释发现，导致潜在健康的器官被认为不适合移植或可能受损的器官被不合适地移植。我们的团队开发了第一个深能够自动量化全局肾小球硬化百分比的学习模型供体肾脏冷冻部分楔形活检的滑动图像。这种创新的方法具有通过提高病理学家效率来改变供体肾脏活检评估的潜力，准确性和精度最终导致了优化的供体器官利用率，减少了医疗保健费用，改善了患者的结果。该项目的目的是建立我们的深度学习自动定量评估作为捐赠肾脏的标准实践在移植之前进行评估。这将通过组建一组专家肾脏来实现病理学家和计算机科学家专门研究机器学习。提案将评估计算机方法的准确性和精度，以量化全球百分比肾小球硬化并将这些结果与当前的护理病理学家进行比较评估。部署深度学习模型以分析整个幻灯片图像的可行性在云上也将进行检查。该STTR的最终产品将是基于网络的平台将深度学习图像分析牢固地部署为工具，以协助病理学家与捐助者肾脏活检评估。