EAGER: Low Cost Field Portable Computational 3D Optical Imaging Biophotonics Sensors for Automated Disease Identification

使用低成本现场便携式传感器的自动疾病识别可能会对公众健康产生重大影响。例如，虽然疟疾是可以预防和治愈的，但在亚洲和非洲欠发达地区识别疟疾患者尤其具有挑战性，因为大多数病例发生在获得医疗保健的农村地区。仅在亚洲，估计每年就有3,600万个病例和约49,000人死亡；根据世卫组织的数据，2013年在非洲，疟疾造成估计584,000人死亡(不确定性范围在367,000至755,000人之间)，主要是非洲儿童。与实验室中用于细胞鉴定的传统生物分子分析方法相比，所提出的低成本、现场便携式传感器方法可以在缺乏实验室基础设施的偏远地点使用。该项目的成功可能会导致预防或控制疾病的爆发。虽然风险很高，但拟议的自动疾病识别系统的成功具有在医学、细胞生物学和显微镜以及公共卫生和消费者安全方面的广泛应用潜力，包括实时废水分析、移动设备的电子医疗保健、环境传感和食品安全监测。这项拟议的工作是一项可行性研究，旨在探索一种变革性的生物光子学传感器，而不需要昂贵的光学元件，或者可以最大限度地降低成本和尺寸，同时实现高性能数据传感能力，以实现准确的自动化疾病识别。这种方法与目前用于疾病诊断的专用医疗机构中使用的敏感、复杂的光学和光子系统截然不同。在这项可行性研究中，光学和光子学设备将被配置在一种新型的便携式光学系统中，该系统具有用于移动设备的新颖的统计算法和计算方法。传感器将使用光源，如激光二极管，来照亮目标。来自目标的衍射光将入射到相位板上，相位板将散射光而不会显著影响其强度；分析前将由图像传感器或摄像机记录结果，图像传感器或摄像机将看到来自光源的散射和直接入射的光的干涉图案，用于自动细胞分析和识别。该项目的成功将为在欠发达地区识别和抗击疾病的紧凑、现场便携和低成本的解决方案奠定基础。

Automated disease identification using low cost field portable sensors can have a substantial public health impact. For example, although malaria is preventable and curable, identification of malaria patients in under developed areas of Asia and Africa is particularly challenging since most cases occur in rural areas where access to healthcare is limited. In Asia alone, an estimated 36 million cases and around 49,000 deaths occur annually; in Africa in 2013, according to WHO, malaria caused an estimated 584,000 deaths (with an uncertainty range of 367,000 to 755,000), mostly among African children. In contrast to conventional bio-molecular analysis for cell identification in laboratory, the proposed low-cost, field portable sensor approach can be used in remote locations lacking laboratory infrastructure. The success of the project can potentially lead to preventing or controlling outbreaks of disease. Although high risk, the success of the proposed automated disease identification system has the potential for widespread applications in medicine, cell biology, and microscopy as well as public health and consumer safety, including real time waste water analysis, e-healthcare with mobile devices, environmental sensing, and food safety monitoring. The proposed work is a feasibility study to explore a transformative biophotonics sensors without expensive optical components, or devices to minimize cost and size while achieving high performance data sensing capabilities for accurate automated disease identification. This approach is a radical departure from the sensitive, complex optical and photonic systems currently used in dedicated healthcare facilities for disease diagnostics. In this feasibility study, optics and photonics devices will be configured in a novel portable optical system with novel statistical algorithms and computational methods for mobile devices. The sensor will use a light source, such as a laser diode, to illuminate a target. The diffracted light from the target will be incident on a phase plate, which will scatter the light without significantly affecting its intensity; the result will be recorded by an image sensor or camera prior to analysis, which will see an interference pattern from the scattered and directly impinging light from source for automated cell analysis and identification. The success of the program will form the basis for a compact, field portable, and low cost solutions in identifying and combating disease in under developed areas.