Maternal mHealth blood hemoglobin analysis with informed deep learning

项目摘要/摘要 血液血红蛋白(HGB)检测是常规患者护理和筛查过程中常见的临床实验室检查。 特别是，血液HGB检测对于贫血的诊断和治疗是必不可少的。全球范围内，超过40%的 孕妇贫血，通过增加贫血对母婴健康产生不利影响 发病率和死亡率。一系列治疗贫血的方法已经很成熟，甚至在 低收入和中等收入国家。在这些环境中，主要的挑战是贫血没有被检测到或 检测到的太晚了。适用于在资源有限的环境中需要进行多次HGB检测的孕妇 怀孕三个月后，传统的有创血液HGB检测不仅痛苦和医源性高，而且价格昂贵 而且往往难以接近。现有的非侵入性血液测量设备和基于智能手机的技术 HGB水平通常依赖昂贵的专门设备和复杂的智能手机附件，因此阻碍了 在资源有限的情况下，从研究到临床实践的实用翻译。根据初步的 由我们的跨学科团队产生的结果，我们假设血液中的HGB水平可以准确和 根据获得的内眼皮(眼睑结膜)的红-绿-蓝(RGB)图像准确预测 使用不带其他附件的智能手机摄像头，并且这款移动健康(MHealth)应用程序 可以在低资源环境下与现有的电子健康记录(EHR)系统完全集成。 具体地说，知情的学习方法将使我们能够结合物理或生物学的理解 将学习算法转化为克服单纯数据驱动的机器学习的局限性。我们的团队， 由光学光谱学和机器学习、生物医学信息学和实施方面的专家组成 科学、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mHealth和数字健康设置开发一种强大、简单、前端的数据采集方法。一个 特定于组织的色域设计和真彩色恢复将提供首个此类系统 实现对血液HGB高度敏感的色彩准确度的方法学。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。的子算法 自动内眼皮划分、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烈非线性 域。在目标3中，我们将把mHealth血液HGB技术与广泛使用的EHR相结合，并评估 后端性能。提议的互联移动健康技术将展示提供 移动性、简单性和经济实惠，可实现快速、可扩展的适应，最大限度地利用当前可用的 资源有限的环境中的资源。我们的工作也可以相互创新，提供先进的 在美国，移动健康和数字健康技术与远程医疗相结合，在农村和家庭环境中使用。

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.