Integration of Artificial Intelligence (AI) and Distributed Ledger Technologies to Improve Interpretability and Reportability for Point-of-Care Medica

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Dengue virus (DENV) is a mosquito-borne viral disease whose global incidence has increased dramatically in the past decade with current estimates indicating an incidence level of up to 390 million infections per year globally. Paper-based microfluidic devices have shown various successes as a point-of-care settings for field use due to their relatively small size (approximately a few mm to 2 cm in length and width), portability and relatively inexpensive cost. However, most uPAD devices are still unable to be interpreted by non-professional users. This problem is compounded by the virus' infections propensity to be under-reported and the low-resource settings these endemic viruses occur in leading to poor rates in seeking medical treatment.An integrated system using artificial intelligence (AI) and distributed ledger technologies could be utilized to tackle these pain points together with microfluidics. Professor Cooper's research group has recently developed an AI platform with blockchain communication for the detection of malaria in Uganda. The research used supervised learning algorithms to identify positive and negative results via photograph with links to could-based learning platform. However, the use of blockchain technologies is energy-intensive and the link to the cloud for data analysis requires constant network access. In this project, we will establish new distributed ledger architectures enabling edge computing (on the device performing the assay without the need for network access). The new platform will be integrating with national digital health platforms, such as DHIS2, to create a (de-)centralized surveillance system automatically, improving the previous identified problem of poor official reporting, which has the potential to lead to new public health measures (e.g. to reduce vector populations). The improved usability of this integrated system would potentially improve interpretability even for medical experts whose point of expertise lie outside of the viral infection itself (e.g., general health workers such as nurses or general practitioners). Overall, this would increase the robustness of healthcare systems management for dengue virus for future endemic outbreaks.