A Deep Learning Model to Quantify Arteriosclerosis in Donor Kidney Biopsies

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还多。肾 移植挽救生命，但供体器官短缺，器官废弃率高 在等待移植的患者中，每天有13人死亡。决定使用还是 丢弃供体肾脏在很大程度上依赖于显微镜下对慢性损害的量化 病理学家。目前的护理标准依赖于人工流程，该流程受 显著的人类变异性和低效，导致潜在的健康肾脏 被丢弃的或可能受损的肾脏被不当移植。我们的团队 开发了第一个深度学习模型来量化供者的全球肾小球硬化百分比 肾脏冰冻切片活检全玻片图像。我们开发了一个基于云的平台来应用 深度学习模型在6分钟内分析肾活检全片图像 准确度和精密度等于或高于目前病理学家的护理标准。我们有 我还开发了一个深度学习模型来量化供体肾活检的间质纤维化 完整的幻灯片图像。这种创新的方法有可能改变捐赠者的肾脏活检。 通过提高病理学家的效率、准确性和精确度进行评估，最终导致 优化供体器官利用，改善患者预后，降低医疗保健成本。 该项目的目标是开发一种深度学习技术，用于量化 动脉硬化，以支持移植前对供体肾脏的评估。这将是 通过召集一支由专业病理学家和计算机科学家组成的团队实现 机器学习。该提案将评估动脉硬化的准确性和精密度 深度学习模型。Trusted Kidney软件平台的功能将是 超越当前可用的产品，改进为商业上可行的解决方案，可满足多种需求 实验室。

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.