Developing a virtual placenta biobank

开发虚拟胎盘生物库

• 批准号: 10040733
• 负责人: Jeffery A Goldstein
• 金额: $ 18.56万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10040733
• 关键词: Advisory Committees; Algorithms; Architecture; Area; Artificial Intelligence; Atlases; Biological; Cells; Cellularity; Child; Chorion; Complex; Data; Decidual Cell; Detection; Diagnosis; Diagnostic; Disease; Doctor of Philosophy; Elements; Endocrine system; Endothelium; Event; Feeds; Fetal Lung; Fibrinoid necrosis; Funding; Gestational Age; Glass; Goals; Hematoma; Hemosiderosis; Histology; Histopathology; Human; Immune system; Infarction; Informatics; Kidney; Lead; Learning; Length; Liver; Lung; Machine Learning; Manuals; Maternal and Child Health; Measurement; Membrane; Mentors; Microscopic; Modeling; Morphology; Organ; Pathogenicity; Pathologic; Pathologist; Pathology; Physicians; Physiological; Physiology; Placenta; Placentation; Pre-Eclampsia; Pregnancy; Premature Birth; Radar; Recording of previous events; Reporting; Reproducibility; Research; Resources; Scanning; Schedule; Science; Scientist; Second Pregnancy Trimester; Shapes; Skin; Slide; Specimen; Spiral Artery of the Endometrium; Structure; Techniques; Testing; Thinness; Third Pregnancy Trimester; Time; Tissues; Training; Umbilical cord structure; United States National Institutes of Health; Universities; Variant; Villous; Villus; Yang; algorithm training; biobank; career development; cell type; chorionic plate; digital; digital pathology; fetal; health of the mother; improved; interest; intrahepatic cholestasis of pregnancy; machine learning algorithm; macrophage; meetings; microscopic imaging; novel; online repository; pediatrician; premature; professor; supplemental instruction; tool; trophoblast; virtual; whole slide imaging

Project Summary / Abstract The placenta is the first organ to develop and functions as the fetal lung, kidney, gut, skin, immune and endocrine systems. It is the cause of, and reflects changes from, most diseases in pregnancy, yet remains understudied. This career development proposal will train me in the tools and practice of digital pathology, while I apply them to the placenta with the hypothesis that there are reproducible, quantitative changes in the placenta that can be modeled and used to identify abnormalities via artificial intelligence (AI). I will create a publicly available atlas of microscopically normal placentas from throughout the 2nd and 3rd trimesters. Whole slide imaging will be performed on microscopic slides of placentas from the beginning of the 2nd trimester (13 weeks) through post-term (42 weeks). I will lead a team to annotate tissue type, structures, and cells. Algorithms will be trained to replicate the manual annotations. To study the changes in the placenta over time, automated measurements will be performed to identify changes in shape, size, and cellularity of placental structures that correlate with gestational age. This research can be used to develop a model of placental development and study prematurity. I will demonstrate detection of diseases of pregnancy, using preeclampsia (PreE) as an example. Placentas with microscopic changes classically seen in PreE will be scanned and annotated and algorithms trained and tested to identify them. Like many diseases of pregnancy, placental changes in PreE are variable and sometimes absent. Slides from PreE cases with no microscopic abnormalities will be scanned and examined using the quantitative parameters developed for normal placentas, testing the hypothesis that one or more of them will significantly differ between PreE cases and gestational age- matched controls. I am an Assistant Professor of Pathology at Northwestern University with an emerging focus in informatics and machine learning for diseases of pregnancy. The mentor for this project is Lee D.A. Cooper, PhD, an expert in digital pathology and machine learning. The co-mentor is David M. Aronoff, MD, an expert in maternal-child health. Mentor and co-mentor both have a history of NIH funding and graduating mentees to independence. The advisory committee consists of a digital pathology expert (Gutman), a pediatrician (Mestan) and a pathologist physician scientist (Yang). They have proposed an aggressive schedule of one-on-one meetings, coursework, seminars, and scientific meetings to supplement learning by doing the science. Completion of these studies will build my expertise in the application of machine learning to placental pathology while creating a new, publicly- accessible tool for the rapid assessment and understanding of organ structure and function with great potential to improve maternal-child health.

项目总结/摘要 胎盘是最早发育的器官，起着胎儿肺、肾、肠、皮肤、免疫和内分泌等功能 系统.它是怀孕期间大多数疾病的原因，并反映了这些疾病的变化，但仍然研究不足。 这个职业发展建议将培训我在数字病理学的工具和实践，而我应用它们 假设胎盘中存在可重复的定量变化， 通过人工智能（AI）建模并用于识别异常。 我将创建一个公开可用的显微镜下正常胎盘的图集，从整个第二和第三季度开始， 第三个三个月从开始时起，将对胎盘的显微镜载玻片进行全载玻片成像。 第二个三个月（13周）到后期（42周）。我将带领一个小组对组织类型，结构， 和细胞。将训练算法以复制手动注释。研究胎盘的变化 随着时间的推移，将进行自动测量，以确定形状，大小和细胞结构的变化， 与胎龄相关的胎盘结构。这项研究可以用来开发一个模型， 胎盘发育和研究早产。我将演示妊娠疾病的检测，使用 先兆子痫（PreE）为例。胎盘与显微镜下的变化，典型地看到在PreE将 扫描和注释，并训练和测试算法来识别它们。像许多妊娠疾病一样， PreE的胎盘变化是可变的，有时不存在。来自PreE病例的载玻片， 将使用为正常胎盘开发的定量参数扫描和检查异常， 检验其中一个或多个在PreE病例和胎龄之间存在显著差异的假设， 匹配的控制。 我是西北大学病理学助理教授，重点是信息学， 机器学习用于妊娠疾病。本项目的导师是Lee D.A.库珀博士，他是 数字病理学和机器学习。共同导师是大卫M。Aronoff，医学博士，一位母婴专家， 健康导师和共同导师都有NIH资助和毕业学员独立的历史。的 咨询委员会由一名数字病理学专家（古特曼）、一名儿科医生（梅斯坦）和一名病理学家组成 医学科学家（Yang）。他们提出了一个积极的时间表，一对一的会议，课程， 研讨会和科学会议，通过做科学来补充学习。完成这些研究将 建立我在机器学习应用于胎盘病理学方面的专业知识，同时创建一个新的，公开的- 快速评估和了解器官结构和功能的工具，具有巨大的潜力 改善母婴健康。