Integrated rapid-diagnostic-test reader platform on a cellphone.

We demonstrate a cellphone-based rapid-diagnostic-test (RDT) reader platform that can work with various lateral flow immuno-chromatographic assays and similar tests to sense the presence of a target analyte in a sample. This compact and cost-effective digital RDT reader, weighing only ~65 g, mechanically attaches to the existing camera unit of a cellphone, where various types of RDTs can be inserted to be imaged in reflection or transmission modes under light-emitting diode (LED)-based illumination. Captured raw images of these tests are then digitally processed (within less than 0.2 s per image) through a smart application running on the cellphone for validation of the RDT, as well as for automated reading of its diagnostic result. The same smart application then transmits the resulting data, together with the RDT images and other related information (e.g., demographic data), to a central server, which presents the diagnostic results on a world map through geo-tagging. This dynamic spatio-temporal map of various RDT results can then be viewed and shared using internet browsers or through the same cellphone application. We tested this platform using malaria, tuberculosis (TB) and HIV RDTs by installing it on both Android-based smartphones and an iPhone. Providing real-time spatio-temporal statistics for the prevalence of various infectious diseases, this smart RDT reader platform running on cellphones might assist healthcare professionals and policymakers to track emerging epidemics worldwide and help epidemic preparedness.

Distributed medical image analysis and diagnosis through crowd-sourced games: a malaria case study.

In this work we investigate whether the innate visual recognition and learning capabilities of untrained humans can be used in conducting reliable microscopic analysis of biomedical samples toward diagnosis. For this purpose, we designed entertaining digital games that are interfaced with artificial learning and processing back-ends to demonstrate that in the case of binary medical diagnostics decisions (e.g., infected vs. uninfected), with the use of crowd-sourced games it is possible to approach the accuracy of medical experts in making such diagnoses. Specifically, using non-expert gamers we report diagnosis of malaria infected red blood cells with an accuracy that is within 1.25% of the diagnostics decisions made by a trained medical professional.