Maternal mHealth blood hemoglobin analysis with informed deep learning

通过知情深度学习进行孕产妇 mHealth 血液血红蛋白分析

• 批准号: 10566426
• 负责人: Young L Kim
• 金额: $ 47.86万
• 依托单位: PURDUE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2027-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10566426
• 关键词: Affect; African; Algorithms; Anemia; Biological; Blood; Cellular Phone; Clinical; Clinical Data; Color; Complex; Computational algorithm; Data; Devices; Diagnosis; Diagnostic; Disease; Electronic Health Record; Environment; Equipment; Eyelid structure; Fetal health; Foundations; Goals; Health Services Accessibility; Health Technology; Hemoglobin; Hemoglobin concentration result; Home; Hyperbilirubinemia; Iatrogenesis; Image; Impaired health; Infusion procedures; Kenya; Laboratories; Learning; Light; Machine Learning; Malaria; Manuals; Maternal Health; Measures; Methodology; Methods; Mobile Health Application; Modeling; Morbidity - disease rate; Optics; Outcome; Oxygen; Pain; Patient Care; Performance; Perfusion; Peripheral; Persons; Population; Pregnancy; Pregnant Women; Process; Public Health; Pulse Oximetry; Recovery; Research; Resource-limited setting; Resources; Rural; Sickle Cell Anemia; Site; Spectrum Analysis; Structure of palpebral conjunctiva; Technology; Telemedicine; Testing; Tissues; Translational Research; Work; automated algorithm; biomedical informatics; clinical practice; cost; data acquisition; deep learning; design; digital; digital health; electronic health record system; health of the mother; implementation science; improved; innovation; learning algorithm; low and middle-income countries; mHealth; machine vision; mobile application; mortality; next generation; patient screening; point of care; reconstruction; remote patient monitoring; sensor; signal processing

PROJECT SUMMARY/ABSTRACT Blood hemoglobin (Hgb) testing is a common clinical laboratory test during routine patient care and screening. In particular, blood Hgb tests are essential for the diagnosis and management of anemia. Globally, over 40% of pregnant women are anemic, adversely affecting maternal and fetal health outcomes through increased morbidity and mortality. A range of treatments for anemia are well-established and readily available even in low- and middle-income countries. In these settings, the main challenge is that anemia is not detected or detected too late. For pregnant women in resource-limited settings who require several Hgb tests during all trimesters, conventional invasive blood Hgb tests are not only painful and iatrogenic, but are also expensive and often inaccessible. Existing noninvasive devices and smartphone-based technologies for measuring blood Hgb levels often rely on costly specialized equipment and complex smartphone attachments, thus hampering practical translation from research to clinical practice in resource-limited settings. Based on the preliminary results generated by our transdisciplinary team, we hypothesize that blood Hgb levels can be accurately and precisely predicted from a red-green-blue (RGB) image of the inner eyelid (palpebral conjunctiva) acquired using a smartphone camera with no additional attachments, and that this mobile health (mHealth) application can be fully integrated with an existing electronic health record (EHR) system in low-resource settings. Specifically, an informed learning approach will enable us to incorporate a physical or biological understanding into the learning algorithms to overcome the limitations of purely data-driven machine learning. Our team, consisting of experts in optical spectroscopy and machine learning, biomedical informatics and implementation science, and maternal and public health, proposes three aims to achieve the project goals. In Aim 1, we will develop a robust, simple, frontend data acquisition method for various mHealth and digital health settings. A tissue-specific color gamut design and true color recovery will provide the first-of-its-kind systematic methodology to realize color accuracy that will be highly sensitive to blood Hgb. In Aim 2, we will perfect the core mHealth computational algorithm using clinical data of black African pregnant women. Sub-algorithms of automated inner eyelid demarcation, advanced spectral learning, and blood Hgb content computation will enable fully automated, highly accurate, and precise blood Hgb estimations. Tissue optics-informed spectral learning will capture strong nonlinearity between RGB values and spectral intensity directly in the spectral domain. In Aim 3, we will integrate mHealth blood Hgb technology with a widely used EHR and evaluate the backend performance. The proposed connected mHealth technology will demonstrate the possibility of offering mobility, simplicity, and affordability for rapid and scalable adaptation, maximizing the currently available resources in resource-limited settings. Our work can also provide reciprocal innovation to offer advanced mHealth and digital health technologies combined with telemedicine in rural and at-home settings in the US.

项目摘要/摘要 血液血红蛋白（HGB）测试是常规患者护理和筛查期间常见的临床实验室测试。 特别是，血液HGB测试对于贫血的诊断和管理至关重要。全球，超过40％ 孕妇是贫血的，对孕产妇和胎儿健康的影响通过增加而受到不利影响 发病率和死亡率。贫血的一系列治疗范围已建立了良好，即使在 低收入和中等收入国家。在这些情况下，主要的挑战是未发现贫血或 检测到为时已晚。适用于资源有限的设置中的孕妇 三个常规的侵入性血液HGB测试不仅疼痛和医源性，而且还很昂贵 而且常常无法访问。现有的无创设备和基于智能手机的技术来测量血液 HGB水平通常依赖于昂贵的专业设备和复杂的智能手机附件，从而阻碍 从研究到资源有限设置的临床实践的实际翻译。基于初步 我们的跨学科团队产生的结果，我们假设血液HGB水平可以准确，并且 从获得的内眼睑（Palpebral Conjunctiva）的红绿色蓝色（RGB）图像中精确预测 使用没有其他附件的智能手机相机，并且该移动健康（MHealth）应用程序 可以在低资源设置中与现有的电子健康记录（EHR）系统完全集成。 具体而言，明智的学习方法将使我们能够融入物理或生物学理解 进入学习算法，以克服纯粹数据驱动的机器学习的局限性。我们的团队， 由光谱和机器学习，生物医学信息学和实施方面的专家组成 科学以及孕产妇和公共卫生提出了三个旨在实现项目目标的目标。在AIM 1中，我们将 为各种MHealth和数字健康设置开发强大，简单，前端的数据采集方法。一个 组织特异性的颜色域设计和真实的颜色恢复将提供首个系统的系统 实现对血液HGB高度敏感的颜色准确性的方法。在AIM 2中，我们将完善 使用黑人非洲孕妇的临床数据核心MHealth计算算法。的子算法 自动内眼睑分界，高级光谱学习和血液HGB含量计算将 实现完全自动化，高度准确且精确的血液HGB估计。组织光学的光谱 学习将在光谱中直接捕获RGB值和光谱强度之间的强烈非线性 领域。在AIM 3中，我们将将MHealth血液HGB技术与广泛使用的EHR整合在一起，并评估 后端性能。拟议的互联MHealth技术将证明提供的可能性 快速和可扩展适应的机动性，简单性和负担能力，最大化当前可用的 资源有限设置中的资源。我们的工作还可以提供相互的创新以提供高级 MHealth和Digital Health Technologies与美国农村和家居环境中的远程医疗相结合。