Maternal mHealth blood hemoglobin analysis with informed deep learning

项目总结/摘要 血液血红蛋白（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测试是必不可少的诊断和贫血的管理。全球超过40%的 孕妇贫血，对孕产妇和胎儿的健康结果产生不利影响， 发病率和死亡率。一系列治疗贫血的方法是完善的，即使在 低收入和中等收入国家。在这些情况下，主要的挑战是没有检测到贫血， 发现得太晚了。对于在资源有限的环境中需要在整个过程中进行多次Hgb测试的孕妇， 传统的侵入性血液Hgb测试不仅是痛苦的和医源性的，而且价格昂贵 而且常常难以接近。现有的非侵入性设备和基于智能手机的血液测量技术 Hgb水平通常依赖于昂贵的专门设备和复杂的智能手机附件，从而阻碍了 在资源有限的情况下，从研究到临床实践的实际翻译。根据初步 我们的跨学科团队得出的结果，我们假设血液Hgb水平可以准确地， 从所获取的内眼睑（眼睑结膜）的红-绿-蓝（RGB）图像精确预测 使用没有附加附件的智能手机摄像头，这种移动的健康（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，一个知情的学习方法将使我们能够纳入物理或生物的理解 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 为各种移动医疗和数字医疗环境开发一种强大、简单的前端数据采集方法。一 组织特定的色域设计和真实色彩恢复将提供第一个同类系统 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 核心mHealth计算算法使用非洲黑人孕妇的临床数据。子算法 自动内眼睑划分、高级光谱学习和血液Hgb含量计算将 实现全自动、高度准确和精确的血液Hgb估计。组织光学信息光谱 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， 域在目标3中，我们将整合mHealth血液Hgb技术与广泛使用的EHR，并评估 后端性能。拟议的互联移动健康技术将展示提供以下服务的可能性： 移动性、简单性和经济性，可实现快速和可扩展的适应，最大限度地提高当前可用 在资源有限的情况下。我们的工作还可以提供互惠创新， 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。

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.