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.

项目摘要 /摘要 胎盘是第一个发育和起作用的器官，作为胎儿肺，肾脏，肠道，皮肤，免疫和内分泌 系统。它是造成怀孕大多数疾病的变化的原因，但仍被研究不足。 这项职业发展建议将在我应用数字病理学的工具和实践中训练我 对胎盘的假设，即胎盘中有可再现的定量变化可以是 建模并用于通过人工智能（AI）识别异常。 我将在整个第二个中创建一个公开可用的显微镜​​正常胎盘的地图 第三个三重。从开始时，将在胎盘的微观载玻片上进行整个幻灯片成像 第二个孕期（13周）到学期后（42周）。我将带领一个团队注释组织类型，结构， 和细胞。算法将接受培训以复制手动注释。研究胎盘的变化 随着时间的流逝，将进行自动测量，以确定形状，大小和细胞的变化 与胎龄相关的胎盘结构。这项研究可用于开发一个模型 胎盘发展和研究早产。我将证明使用怀孕疾病的发现 子痫前期（PREE）为例。在Pree中经典看到的具有微观变化的胎盘将是 经过扫描，注释和算法训练和测试以识别它们。像许多怀孕疾病一样 胎盘的胎盘变化是可变的，有时不存在。没有显微镜的pree病例的幻灯片 使用针对正常胎盘开发的定量参数扫描和检查异常， 检验以下假设：其中一个或多个在预科病例和胎龄之间会有显着差异 匹配的控件。 我是西北大学病理学助理教授，在信息学上有新兴的重点 怀孕疾病的机器学习。该项目的导师是Lee D.A.库珀博士，专家 数字病理学和机器学习。该院长是母亲的专家David M. Aronoff，医学博士 健康。导师和同事都有NIH资金的历史和毕业的受训者独立。这 咨询委员会由数字病理学专家（Gutman），儿科医生（Mestan）和病理学家组成 医师科学家（Yang）。他们提出了一对一的会议，课程工作的积极时间表 研讨会和科学会议，通过进行科学来补充学习。这些研究的完成将 建立我在机器学习到胎盘病理学的应用方面的专业知识，同时创建新的，公开的 - 可访问的工具，以快速评估和了解器官结构和功能，具有巨大潜力 改善母子健康。