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.
