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

• 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
• 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

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.

项目摘要 我们的建议是为了需要开发非侵入性的工具来监测极低出生人口的贫血 体重(VLBW；出生体重和1,500克)并减少常规痛苦、有创采血的次数 可能改变婴儿神经发育和行为的程序(静脉抽液)。最近，一款新的智能手机 应用程序[Mannino等人，自然通信，9,4924(2018)]，收集和分析 已经开发了来自患者的指甲照片和图像元数据来预测血红蛋白水平。这个 APP使用强大的多元线性回归模型，该模型结合了汇总颜色强度值(平均值 跨像素)以及由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。虽然目前的APP算法简单且易于实现，但有以下几种 明显的局限性。首先，它没有充分利用指甲照片中丰富的空间信息 计算一个简单的平均值。其次，目前的算法仅使用成人进行训练，这些成年人的临床 特征与婴儿有很大的不同。APP预测结果与血液的符合率为95% 成人的基于样本的血红蛋白水平报告为2.4g/dL，高于临床实验室 改进修正规范差异为1.0g/dL，并可能增加VLBW婴儿的 微小的、非特定的指甲床。这种严格的误差要求和总体的异质性要求更高 比当前应用程序使用的算法更准确和量身定做的算法。最后，将应用程序应用到的框架 目前缺乏对极低出生体重儿进行纵向护理的最大限度减少抽血。 基于这些考虑，我们建议(目标1)开发一种新的图像分析算法(IAA)，该算法能够产生 无创、准确、稳定的血红蛋白水平预测。IAA将以一个新的主体为基础 成分分析方法，提供了一种非参数和简约的手段来联合建模高度的 多维照片和图像元数据，同时充分利用它们的空间结构和共同变化的图案。 我们还将考虑一种新的偏最小二乘方法作为替代方法。我们将进行培训和验证 IAA基于成人数据和VLBW婴儿数据。在目标2中，我们将开发一种新的聚类方法来 研究指甲照片和图像元数据的子群体结构，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形成一个非侵入性的图像- 通过识别具有高贫血风险的极低出生体重儿群，基于筛查工具。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 在整个护理过程中最大限度地减少极低出生体重儿抽血次数的首要临床目标 连续体。我们的建议将使用在三级新生儿重症监护中监测的极低出生体重儿的数据 在亚特兰大的单位。所提出的方法一般适用于具有不同和 临床数据的复杂模式。