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多。肾 移植是挽救生命的方法，但供体器官短缺和器官丢弃率高 导致每天有13名等待移植的病人死亡。决定使用或 丢弃供体肾脏在很大程度上依赖于慢性损伤的显微镜定量， 病理学家目前的护理标准依赖于手动过程， 显著的人类变异性和低效率，导致潜在的健康肾脏被 被丢弃和潜在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来量化供体中全球肾小球硬化的百分比 肾脏冰冻切片活检全切片图像。我们开发了一个基于云的平台， 深度学习模型可以在6分钟内分析肾活检全切片图像， 准确度和精确度等于或高于当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 整个幻灯片图像。这种创新的方法有可能改变供体肾活检 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的评估。这将是 通过组建一个专家病理学家和计算机科学家团队， 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 将当前可用产品改进为商业上可行的解决方案， laboratories.

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.