MRI and Deep Learning for Early Prediction of Neurodevelopmental Deficits in Very Preterm Infants

MRI 和深度学习用于早期预测极早产儿神经发育缺陷

基本信息

批准号：
10028428
负责人：
Lili He
金额：
$ 53.46万
依托单位：
CINCINNATI CHILDRENS HOSP MED CTR
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：
2020-09-30 至 2024-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10028428
关键词：
5 year old Address Adult Age Anatomic Models Anatomy Artificial Intelligence Biological Birth Brain Child Child Welfare Clinical Clinical Data Cognitive Communities Crowding Data Data Set Detection Diagnosis Diffusion Magnetic Resonance Imaging Dimensions Distant Early Diagnosis Early Intervention Early identification Family Functional Magnetic Resonance Imaging Funding Gestational Age Goals Health Image Image Analysis Individual Infant Institution Intervention Knowledge Language Magnetic Resonance Imaging Maps Medical Methods Modeling Motor Neonatal Intensive Care Units Neurodevelopmental Deficit Neurodevelopmental Problem Neuronal Plasticity Outcome Pathologic Pathology Patients Pattern Performance Personal Satisfaction Pregnancy Premature Infant Psychological Transfer Public Health Quality of life Research Rest Risk Risk Factors Risk stratification Series Structural defect Structure Techniques Therapeutic Intervention Tissues Training United States United States National Institutes of Health Work accurate diagnosis base brain tissue classification algorithm clinical risk cohort computer program connectome convolutional neural network deep learning deep neural network feeding high dimensionality high risk improved insight large datasets multimodality multitask neonate nervous system disorder neurodevelopment novel outcome forecast outcome prediction precision medicine predictive modeling repository risk prediction model social success

项目摘要

Project Summary/Abstract About 100,000 very preterm infants (VPI; ≤32 weeks gestational age) are born every year in the United States. Up to 35% develop noteworthy neurodevelopmental deficits, thereby increasing their risk for poor educational, health, and social outcomes. Unfortunately, neurodevelopmental deficits cannot currently be reliably diagnosed until 3 to 5 years of age. The imminent challenge lies in early identification of infants that are more likely to develop later deficits. Advances in magnetic resonance imaging (MRI) and deep learning (DL) provide means to address this challenge. Application of DL to infant brain MRI data can open up new windows into early prediction of neurodevelopmental outcomes in at-risk infants and facilitate the move towards precision medicine. Our objective is to apply DL to MRI acquired at term equivalent age for early prediction of neurodevelopment deficits (cognitive, language, and motor) at age 2 in VPI. Our group has identified three key components necessary for accurate prognostic models of later neurodevelopment. DL analysis of 1) anatomical features derived from structural MRI (sMRI) allowing detection of brain structural abnormalities and tissue pathologies; 2) brain connectivity features derived from resting-state functional MRI (rs-fMRI) and diffusion MRI (dMRI) giving insights into atypical brain connectivity patterns; and 3) integration of anatomical and connectivity features, thus enhancing abnormal neurodevelopment prediction. In this project, we will dedicate our efforts in accomplishing the following specific aims. In Aim 1 and Aim 2, we will develop deepAna and deepConn models analyzing anatomical and connectivity features independently to predict adverse neurodevelopmental outcomes. By decoding each model, we will identify, validate and disseminate a series of the most discriminative anatomical and connectivity features to the research community. In Aim 3, we will develop an ensemble deepAnaConn model analyzing both anatomical and connectivity features, together with clinical risk factors, for early prediction of neurodevelopmental deficits. This model will help clinicians to predict later outcomes for those at-risk prematurely born infants before initial neonatal intensive care unit discharge. We will validate the models using both internal and independent external data and will open the ‘black-box’ of DL to aid interpretation of imaging and clinical findings. The techniques we develop are expected to improve the modelling fidelity in medical diagnosis/ prognosis in the same way as DL has revolutionized other fields. The DL models we develop will not only benefit early detection of neurodevelopmental deficits in VPI, but also likely benefit individuals with other neurodevelopmental and neurological diseases. This study will significantly impact public health because it will allow clinicians to target clinical and experimental intervention therapies to the most at-risk infants during periods of optimal neuroplasticity, and thus ultimately improve medical outcomes and patient well-being.

项目概要/摘要美国每年约有 100,000 名极早产儿（VPI；胎龄≤32 周）出生。高达 35% 的人出现显着的神经发育缺陷，从而增加了他们接受不良教育的风险，不幸的是，目前无法可靠地诊断神经发育缺陷。 3 至 5 岁之前，迫在眉睫的挑战在于及早识别更有可能发生这种情况的婴儿。磁共振成像（MRI）和深度学习（DL）的进步提供了手段。为了应对这一挑战，将深度学习应用于婴儿脑部 MRI 数据可以为早期研究开辟新的窗口。预测高危婴儿的神经发育结果并促进精确化我们的目标是将深度学习应用于足月同等年龄获得的 MRI，以进行早期预测。我们的小组在 VPI 中确定了 2 岁时的神经发育缺陷（认知、语言和运动）。 1) 后续神经发育的准确预后模型所必需的组件。来自结构 MRI (sMRI) 的解剖学特征可以检测大脑结构异常和组织病理学；2) 来自静息态功能 MRI (rs-fMRI) 的大脑连接特征和扩散 MRI (dMRI) 可深入了解非典型大脑连接模式；3) 解剖学整合；和连接特征，从而增强异常神经发育预测。在目标 1 和目标 2 中，我们将致力于实现以下具体目标。和 deepConn 模型独立分析解剖和连接特征以预测不良反应通过解码每个模型，我们将识别、验证和传播一系列的结果。在目标 3 中，我们将向研究界提供最具辨别力的解剖学和连接特征。开发一个综合 deepAnaConn 模型，分析解剖和连接特征，以及临床危险因素，用于神经发育缺陷的早期预测，该模型将帮助信徒进行预测。那些有风险的早产儿在最初的新生儿重症监护病房出院之前的后续结果。我们将使用内部和独立的外部数据验证模型，并将打开我们开发的技术有望改进，以帮助解释影像和临床结果。医学诊断/预后中的建模保真度就像深度学习彻底改变了其他领域一样。我们开发的深度学习模型不仅有利于早期检测 VPI 中的神经发育缺陷，而且这项研究可能会使患有其他神经发育和神经系统疾病的个体受益。影响公共健康，因为它将允许针对临床和实验干预疗法在神经可塑性时期最危险的最佳婴儿，从而最终改善医疗结果和患者的福祉。