Developing a virtual placenta biobank

开发虚拟胎盘生物库

基本信息

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
项目类别：
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

项目摘要

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.

项目概要/摘要胎盘是第一个发育的器官，其功能相当于胎儿的肺、肾、肠道、皮肤、免疫和内分泌系统。它是大多数妊娠期疾病的原因，并反映了其变化，但尚未得到充分研究。这个职业发展建议将在我应用数字病理学的工具和实践时对我进行培训胎盘的假设是胎盘中存在可重复的定量变化通过人工智能 (AI) 进行建模并用于识别异常情况。我将创建一个公开的图集，其中包含整个 2 个月和 2 个月的显微镜下正常胎盘。第三个三个月。从一开始就将在胎盘的显微载玻片上进行全载玻片成像第二个三个月（13 周）到产后（42 周）。我将带领一个团队注释组织类型、结构、和细胞。将训练算法来复制手动注释。研究胎盘的变化随着时间的推移，将进行自动测量来识别形状、大小和细胞结构的变化胎盘结构与胎龄相关。这项研究可用于开发一个模型胎盘发育和研究早产。我将演示如何检测妊娠疾病，使用以先兆子痫（PreE）为例。具有 PreE 中常见的微观变化的胎盘将是扫描和注释，并训练和测试算法来识别它们。与许多妊娠疾病一样， PreE 中的胎盘变化是可变的，有时甚至不存在。 PreE 病例的载玻片，无显微镜检查将使用为正常胎盘开发的定量参数来扫描和检查异常情况，检验以下假设：PreE 病例和胎龄之间其中一个或多个将显着不同匹配的控件。我是西北大学病理学助理教授，主要研究信息学和妊娠疾病的机器学习。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。共同导师是母婴专家 David M. Aronoff 医学博士健康。导师和共同导师都有 NIH 资助和毕业学员独立的历史。这咨询委员会由数字病理学专家 (Gutman)、儿科医生 (Mestan) 和病理学家组成医学科学家（杨）。他们提出了一个积极的时间表，包括一对一的会议、课程作业、研讨会和科学会议，通过科学研究来补充学习。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、公开的用于快速评估和了解具有巨大潜力的器官结构和功能的易于使用的工具以改善妇幼健康。