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

Abstract

In infectious disease diagnosis, results need to be rapidly communicated to doctors once testing has been completed, in order for care pathways to be implemented. This is a 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 malaria diagnosis. 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 validate 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.