A placenta-based strategy for improved clinical prediction of fetal growth trajectory using automated image analysis of placental morphology and vascularity

基于胎盘的策略，使用胎盘形态和血管分布的自动图像分析来改进胎儿生长轨迹的临床预测

• 批准号: 10512601
• 负责人: Ipek Oguz
• 金额: $ 60.42万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-05 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10512601
• 关键词: 3D ultrasound; Acoustics; Algorithmic Software; Area; Assessment tool; Award; Biological Markers; Biological Models; Blood; Blood Vessels; Characteristics; Chorion; Clinical; Collaborations; Computers; Custom; Detection; Evaluation; Fetal Growth; Fetal Growth Retardation; Fetal Monitoring; Fetal Weight; Fetus; First Pregnancy Trimester; Gestational Age; Growth; Growth Disorders; Health; Histopathology; Hormonal; Human; Image; Image Analysis; Image Enhancement; Immune; Individual; Infant; Knowledge; Laws; Link; Manuals; Measurement; Measures; Medical Imaging; Metabolic; Methodology; Methods; Modeling; Modernization; Monitor; Morphology; National Institute of Child Health and Human Development; Nutritional; Outcome; PGF gene; Pathologic; Pathology; Pattern; Perinatal; Personal Satisfaction; Physicians; Physics; Placenta; Placental Extracts; Placentation; Play; Postpartum Period; Predictive Factor; Predictive Value; Pregnancy; Prenatal care; Provider; Research; Research Personnel; Risk; Risk Factors; Role; Scientist; Screening procedure; Second Pregnancy Trimester; Serum; Shapes; Site; Small for Gestational Age Infant; Structure; Surface; Techniques; Testing; Time; Ultrasonography; antenatal; antenatal care; automated image analysis; automated segmentation; base; clinical care; deep learning algorithm; density; dynamic system; fetal; healthy pregnancy; histopathological examination; image processing; improved; improved outcome; infant outcome; innovation; interdisciplinary approach; mathematical model; morphometry; novel; novel strategies; open innovation; personalized management; placental morphology; precision medicine; prenatal; prevent; recruit; screening; stillbirth; tool

PROJECT SUMMARY/ABSTRACT Fetal growth restriction is associated with a profound increase in perinatal and even long-term health risk. Antenatal care is key to optimizing outcomes and preventing stillbirth, yet up to half of growth-restricted infants are not identified during pregnancy. The placenta serves a central in maintaining a healthy pregnancy and supporting fetal growth; yet, direct assessment of placental development is glaringly absent from clinical care as there are no practical tools that enable providers to monitor placental development. In recent years, 3D ultrasound (3DUS) has allowed investigators to identify important associations between placental morphology and clinical outcomes using a variety of offline medical image analysis techniques. However, these techniques typically require extensive manual input. Moreover, we have recently developed an innovative tool based on a dynamic model of fetal-placental growth that considers placental growth in the evaluation of fetal growth and can help identify pregnancies at increased risk of growth restriction. However, this tool requires placental volume assessment, which, as mentioned above, remains impractical for clinical use. In this proposal, we will expand and enhance our automated segmentation tools to enable bedside volumetric assessment of the placenta throughout pregnancy. In addition, we will develop novel tools and parameters for assessing placental shape, gross morphology, and vascularity in an effort to identify additional features of placental development that can augment our understanding of placental development and create additional markers of placental health. Taken together, the current proposal leverages an ongoing collaboration between computer scientists and physician-scientists to utilize modern fully automated image analysis methodology to create clinically impactful placental assessment tools that can be integrated into the clinical workflow. The proposed research will allow bedside assessment of placental morphology and vascularity, which can be leveraged into precision medicine approaches and allow for more accurate and reliable surveillance of fetal growth and well-being. Specifically, we will build: 1) Refine and validate a fetal-placental growth model using automated early placental volume and placental histopathology, 2) Extend to include later gestational ages and expand the toolkit to include novel measures of placental shape and vascularity, and 3) create an augmented version of the dynamic model that incorporates the added functionality of our segmentation pipeline, as well as serum biomarkers, to result in a clinically useful tool for monitoring fetal growth. We anticipate that this proposal will significantly change clinical care and create a new, placenta-based paradigm for understanding and managing fetal growth disorders.

项目总结/文摘