Smartphone-based DNA diagnostics for malaria detection using deep learning for local decision support and blockchain technology for security

Abstract

In infectious disease diagnosis, results need to be communicated rapidly to healthcare professionals once testing has been completed so that care pathways can be implemented. This represents a particular challenge when testing in remote, low-resource rural communities, in which such diseases often create the largest burden. Here, we report a smartphone-based end-to-end platform for multiplexed DNA diagnosis of malaria. The approach uses a low-cost paper-based microfluidic diagnostic test, which is combined with deep learning algorithms for local decision support and blockchain technology for secure data connectivity and management. We validated the approach via field tests in rural Uganda, where it correctly identified more than 98% of tested cases. Our platform also provides secure geotagged diagnostic information, which creates the possibility of integrating infectious disease data within surveillance frameworks.