Developing statistical image analysis tools for non-invasive monitoring of anemia in low birth weight infants

开发统计图像分析工具，用于低出生体重儿贫血的无创监测

基本信息

批准号：
10452686
负责人：
AMITA K. MANATUNGA
金额：
$ 53.25万
依托单位：
EMORY UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-01 至 2025-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10452686
关键词：
Address Adult Agreement Algorithmic Analysis Algorithms Anemia Beds Behavior Birth Weight Blood Blood specimen Characteristics Clinical Clinical Data Clinical Laboratory Improvement Amendments Color Communication Complement Complete Blood Count Complex Continuity of Patient Care Data Data Analyses Devices Diagnosis Dimensions Ensure Goals Gold Hemoglobin Hemoglobin concentration result Hemorrhage Heterogeneity Hybrids Image Image Analysis Infant Inflammatory Inflammatory Bowel Diseases Knowledge Laboratories Lead Least-Squares Analysis Linear Regressions Longitudinal Studies Low Birth Weight Infant Measurement Metadata Methods Modality Modeling Monitor Nature Necrotizing Enterocolitis Neonatal Intensive Care Units Pain Pallor Patients Pattern Performance Physicians Physiological Population Premature Birth Principal Component Analysis Procedures Recording of previous events Reporting Research Project Grants Risk Screening procedure Source Structure Structure of nail of finger Subgroup Testing Time Training Venous blood sampling Very Low Birth Weight Infant base clinical decision-making design diagnostic tool high dimensionality improved infant monitoring intestinal injury learning strategy neurodevelopment non-invasive imaging non-invasive monitor noninvasive diagnosis novel postnatal postnatal period prediction algorithm premature recruit response smartphone Application statistical learning tool unsupervised learning user friendly software user-friendly

项目摘要

Project Summary Our proposal is motivated by the need to develop non-invasive tools for monitoring anemia in very low birth weight (VLBW; birth weight < 1,500 grams) and reduce the number of routine painful, invasive blood sampling procedures (phlebotomy) that may alter infant neurodevelopment and behavior. Recently, a new smartphone application [Mannino et al., Nature Communications, 9, 4924 (2018)] that collects and analyzes clinical pallor in patient-sourced fingernail photos and image metadata has been developed to predict hemoglobin levels. The app uses a robust multi-linear regression model that incorporates summary color intensity values (average across pixels) of fingernail photos well as the image metadata generated by the device capturing the image to predict patient's hemoglobin level. While the current app algorithm is simple and easy to implement, there are notable limitations. First, it does not fully leverage the rich spatial information available in fingernail photos by calculating a simple average value. Second, the current algorithm is trained using only adults, whose clinical characteristics are vastly different from infants. The 95% limit of agreement between the app-predicted and blood sample-based hemoglobin level for adults is reported as 2.4 g/dL, which is higher than the Clinical Laboratory Improvement Amendments specification variance of 1.0 g/dL, and will likely increase in VLBW infants given their tiny, non-specific fingernail beds. Such strict error requirements and heterogeneity in populations demand more accurate and tailored algorithms than what the current app employs. Lastly, a framework for applying the app to minimize blood draws across the longitudinal care continuum for VLBW infants is currently lacking. With these considerations, we propose (Aim 1) to develop a new image analysis algorithm (IAA) that produces non-invasive, accurate and stable prediction of hemoglobin level. The IAA will be based on a novel principal component analysis method that provides a non-parametric and parsimonious means to jointly model high- dimensional photos and image metadata, while fully leveraging their spatial structures and co-varying patterns. We will also consider a new partial least squares approach as an alternative method. We will train and validate the IAA based on adult data as well as VLBW infant data. In Aim 2, we will develop a new clustering method to study sub-population structures of fingernail photos and image metadata and study their relationships with the underlying physiological mechanisms of anemia. This approach will allow us to formulate a non-invasive image- based screening tool by identifying clusters of VLBW infants with high anemia risk. In Aim 3, we will develop data-driven tools that leverage longitudinal, patient-level clinical data and IAA predictions to achieve the overarching clinical goal of minimizing the number of blood draws in VLBW infants throughout the care continuum. Our proposal will use the data of VLBW infants monitored at three level III neonatal intensive care units in Atlanta. The proposed methods are generally applicable to a wide variety of settings with diverse and complex modalities of clinical data.

项目概要我们的提议的动机是需要开发非侵入性工具来监测极低出生的贫血症体重（VLBW；出生体重 < 1,500 克）并减少常规痛苦的侵入性血液采样次数可能改变婴儿神经发育和行为的手术（放血术）。最近，新推出的智能手机应用程序 [Mannino et al., Nature Communications, 9, 4924 (2018)] 收集并分析临床苍白患者来源的指甲照片和图像元数据已被开发用于预测血红蛋白水平。这应用程序使用强大的多线性回归模型，其中包含摘要颜色强度值（平均指甲照片的跨像素）以及捕获图像的设备生成的图像元数据预测患者的血红蛋白水平。虽然当前的应用程序算法简单且易于实现，但有显着的局限性。首先，它没有充分利用指甲照片中丰富的空间信息计算简单平均值。其次，当前的算法仅使用成年人进行训练，其临床特征与婴儿有很大不同。应用程序预测与血液之间的一致性上限为 95% 据报告，成人样本血红蛋白水平为 2.4 g/dL，高于临床实验室改进修正案规格方差为 1.0 g/dL，考虑到 VLBW 婴儿的出生率，可能会增加微小的、非特定的指甲床。如此严格的误差要求和群体的异质性要求更多比当前应用程序使用的算法更准确且定制的算法。最后，应用程序的框架目前缺乏在极低出生体重婴儿的纵向护理连续过程中最大限度地减少抽血的方法。考虑到这些因素，我们建议（目标 1）开发一种新的图像分析算法（IAA），该算法产生无创、准确、稳定地预测血红蛋白水平。 IAA 将基于一个新颖的原则组件分析方法提供了一种非参数和简约的方法来联合建模高三维照片和图像元数据，同时充分利用它们的空间结构和共变模式。我们还将考虑新的偏最小二乘法作为替代方法。我们将进行训练和验证 IAA 基于成人数据以及 VLBW 婴儿数据。在目标 2 中，我们将开发一种新的聚类方法研究指甲照片和图像元数据的亚群结构，并研究它们与贫血的潜在生理机制。这种方法将使我们能够制定非侵入性图像- 基于筛查工具，通过识别具有高贫血风险的极低出生体重婴儿群体。在目标 3 中，我们将开发数据驱动的工具，利用纵向、患者级别的临床数据和 IAA 预测来实现总体临床目标是在整个护理过程中尽量减少 VLBW 婴儿的抽血次数连续体。我们的提案将使用在三个三级新生儿重症监护室监测的极低出生体重婴儿的数据亚特兰大的单位。所提出的方法通常适用于具有不同和不同特征的各种环境。临床数据的复杂模式。