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

• 批准号: 10266188
• 负责人: Joseph P Gaut
• 金额: $ 77.51万
• 依托单位: NEWVENTUREIQ, LLC
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-21 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10266188
• 关键词: Adoption; Americas; Artificial Intelligence; Biopsy; Canada; Cessation of life; Chronic; Cicatrix; Clinical; Computer software; Computers; Contracts; Data; Databases; Development; Ensure; Evaluation; Fast Healthcare Interoperability Resources; Fibrosis; Frozen Sections; Funding; Goals; Gold; Graft Survival; Health Care Costs; Human; Interobserver Variability; Kidney; Kidney Diseases; Kidney Transplantation; Knowledge; Laboratories; Letters; Life; Machine Learning; Malignant neoplasm of prostate; Manuals; Measurement; Microscope; Microscopic; Midwestern United States; Modeling; Multivariate Analysis; Online Systems; Organ; Organ Donor; Organ Procurements; Outcome; Pathologist; Pathology; Patient-Focused Outcomes; Patients; Performance; Personal Satisfaction; Phase; Process; Reproducibility of Results; Research Personnel; Savings; Scanning; Scientist; Secure; Services; Slide; Small Business Technology Transfer Research; Specialist; Speed; System; Techniques; Testing; Tissues; Transplantation; Trichrome stain; Trichrome stain method; Trust; United Network for Organ Sharing; Universities; Variant; Washington; Work; analytical tool; base; clinical biomarkers; cloud based; cloud platform; commercial application; cost; deep learning; functional improvement; glomerulosclerosis; image processing; imaging biomarker; improved; innovation; interstitial; kidney biopsy; learning strategy; malignant breast neoplasm; pathology imaging; phase 1 study; predictive modeling; public health relevance; renal damage; shared database; standard of care; technological innovation; tool; whole slide imaging

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 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.

抽象的 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，但是捐赠器官短缺和高器官丢弃率 在等待移植的患者中，每天13例死亡。决定使用或 丢弃供体肾脏在很大程度上依赖于对慢性损害的微观定量 病理学家。当前的护理标准依赖于手动过程 人类的显着可变性和效率低下，导致潜在健康的肾脏是 被丢弃并可能受损的肾脏被不适当地移植。我们的团队 开发了第一个深度学习模型，以量化供体中全球肾小球硬化百分比 肾脏冷冻截面活检全滑动图像。我们开发了一个基于云的平台来应用 深度学习模型，用于分析肾脏活检的整个滑梯图像在不到6分钟的时间内 准确性和精度等于或大于当前护理病理学家的标准。我们有 还开发了一个深度学习模型，以量化供体肾脏活检的间质纤维化 整个幻灯片图像。这种创新的方法有可能改变供体肾脏活检 通过提高病理学家效率，准确性和精度来评估，最终导致 优化的供体器官利用率，改善患者预后以及降低医疗保健费用。 该项目的目的是将我们的深度学习自动化技术作为标准建立 用于在移植之前评估供体肾脏。这将通过组建团队来实现 专业的病理学家和计算机科学家专门研究机器学习。提案 将评估间质纤维化深度学习模型的准确性和精度，使用 关键显微镜发现的自动定量，以开发基于结果的慢性 预测移植结果的伤害评分，并测试深度学习模型的能力 承受使用不同扫描仪并在不同的处理中遇到的变化 实验室。可信赖的肾脏软件平台的功能将得到改善 当前可用的产品成为多个实验室的商业可行解决方案。