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.

项目概要/摘要 血红蛋白 (Hgb) 检测是常规患者护理和筛查期间常见的临床实验室检测。 特别是，血液 Hgb 检测对于贫血的诊断和治疗至关重要。在全球范围内，超过 40% 孕妇贫血，通过增加贫血对母体和胎儿的健康结果产生不利影响 发病率和死亡率。一系列针对贫血的治疗方法已十分完善，即使在某些国家也很容易获得 低收入和中等收入国家。在这些情况下，主要的挑战是贫血未被检测到或 发现得太晚了。对于资源有限环境中需要在整个过程中进行多次 Hgb 检测的孕妇 妊娠期，传统的侵入性血液 Hgb 检测不仅会带来痛苦和医源性，而且价格昂贵 并且经常无法访问。现有的用于测量血液的无创设备和基于智能手机的技术 Hgb 水平通常依赖于昂贵的专用设备和复杂的智能手机附件，从而阻碍 在资源有限的环境中从研究到临床实践的实际转化。根据初步 我们的跨学科团队得出的结果，我们假设血液 Hgb 水平可以准确且 根据获取的内眼睑（睑结膜）的红绿蓝 (RGB) 图像精确预测 使用智能手机摄像头，无需额外附件，并且此移动健康 (mHealth) 应用程序 可以在资源匮乏的环境中与现有的电子健康记录 (EHR) 系统完全集成。 具体来说，知情的学习方法将使我们能够融入物理或生物理解 融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， 由光谱学和机器学习、生物医学信息学和实施方面的专家组成 科学、孕产妇和公共卫生提出了实现项目目标的三个目标。在目标 1 中，我们将 为各种移动医疗和数字健康环境开发强大、简单的前端数据采集方法。一个 组织特异性色域设计和真实色彩恢复将提供首创的系统性 实现对血液 Hgb 高度敏感的颜色准确性的方法。在目标 2 中，我们将完善 使用非洲黑人孕妇的临床数据的核心 mHealth 计算算法。子算法 自动内眼睑分界、先进的光谱学习和血液 Hgb 含量计算将 实现全自动、高度准确和精确的血液 Hgb 估算。组织光学光谱 学习将直接在光谱中捕获 RGB 值和光谱强度之间的强非线性 领域。在目标 3 中，我们将 mHealth 血 Hgb 技术与广泛使用的 EHR 相结合，并评估 后端性能。拟议的互联移动医疗技术将展示提供以下服务的可能性： 移动性、简单性和可承受性，可实现快速和可扩展的适应，最大限度地利用当前可用的资源 资源有限环境中的资源。我们的工作还可以提供互惠创新，以提供先进的 移动医疗和数字医疗技术与美国农村和家庭环境中的远程医疗相结合。