Low-Cost and Portable Smartphone-Based Biosensors for Medical Diagnoses

Abstract

Rapid advances in technologies have enhanced healthcare devices and scientific tools to become more and more powerful, allowing observation in micro/nanoscale with high precision and accuracy. Nevertheless, one of the biggest issues is that not all people can easily afford or have access to these tools. Many healthcare devices or diagnostic equipment can be very expensive and time-consuming, requiring trained operators, a large space, and a complex laboratory setup. Clearly, it is not easy for the general public to access these kinds of tools and technologies, especially in limited-resource areas where people may have low income or an inadequate number of trained personnel. To overcome these limitations, my interest is to apply biosensing technologies to develop low-cost medical diagnostic devices that are easily accessible, portable, and easy to use. These can be achieved by utilizing inexpensive materials, implemented with available sensors on the smartphone, and improved using data processing. Two main projects that I have worked on are: 1. SARS-CoV-2 detection assays using (method 1) flow characteristics analysis and (method 2) smartphone-based fluorescence microscopic imaging device for particle counting, and 2. skin microbiome classification using multispectral light sources and autofluorescence imaging. In SARS-CoV-2 detection method 1, we indirectly detect the virus presence via the flow behavior of the antibody conjugated fluorescence particle solution on the sample preloaded paper-based microfluidic channel. For SARS-CoV-2 detection method 2, utilizing the same platform, we aimed to directly visualize the fluorescence signal from the immunoagglutination results on the paper-based microfluidic chip, which has the potential to be more sensitive than method 1, and in this method, I have focused on developing a low-cost and portable device to yield comparable results as the conventional microscope. For the last project, using a similar concept as in method 2, we developed a low-cost and non-invasive technique to classify bacterial species based on their autofluorescence by using multiple light sources and inexpensive color filter films, a smartphone camera, and a data processing algorithm.