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

使用低成本的现场便携式传感器进行自动疾病识别可能会对公共卫生产生重大影响。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机会有限的农村地区。仅在亚洲，估计每年就有3 600万例病例和约49 000人死亡;据世卫组织称，2013年非洲疟疾估计造成584 000人死亡（不确定范围为367 000至755 000人），其中大多数是非洲儿童。与实验室中用于细胞鉴定的常规生物分子分析相比，所提出的低成本、现场便携式传感器方法可用于缺乏实验室基础设施的偏远地区。该项目的成功可能导致预防或控制疾病的爆发。虽然风险很高，但所提出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广泛应用的潜力，包括真实的时间废水分析、使用移动的设备的电子医疗保健、环境传感和食品安全监测。拟议的工作是一项可行性研究，以探索一种变革性的生物光子传感器，而无需昂贵的光学元件或设备，以最大限度地降低成本和尺寸，同时实现高性能的数据传感能力，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的敏感、复杂的光学和光子系统截然不同。在本可行性研究中，光学和光子器件将被配置在一个新型的便携式光学系统中，该系统具有用于移动的设备的新型统计算法和计算方法。传感器将使用光源，如激光二极管，以照亮目标。来自目标的衍射光将入射在相位板上，相位板将散射光而不显著影响其强度;结果将在分析之前由图像传感器或相机记录，其将从来自源的散射和直接照射光看到干涉图案，用于自动化细胞分析和鉴定。该计划的成功将为在欠发达地区识别和防治疾病的紧凑、现场便携和低成本解决方案奠定基础。

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.