Supporting Smartphone-Based Image Capture of Rapid Diagnostic Tests in Low-Resource Settings

Chunjong Park, Alex Mariakakis, Jane Yang, Diego Lassala, Yasamba Djiguiba, Youssouf Keita, Hawa Diarra, Beatrice Wasunna, Fatou Fall, Marème Soda Gaye, Bara Ndiaye, Ari Johnson, Isaac Holeman, Shwetak N. Patel

Abstract: Rapid diagnostic tests (RDTs) provide point-of-care medical diagnosis without sophisticated laboratory equipment, making them especially useful for community health workers (CHWs). Because the procedure for completing a malaria RDT is error-prone, CHWs are often asked to carry completed RDTs back to their supervisors. Doing so makes RDTs susceptible to deterioration and introduces inefficiencies in the CHWs' workflow. In this work, we propose a smartphone-based RDT capture app, RDTScan, that facilitates the collection of high-quality RDT images to support CHWs in the field. RDTScan does not require an external adapter to control the image capture environment, but instead provides real-time guidance using image processing to obtain the best image possible. During our evaluation study, we found that RDTScan had 98.1% sensitivity and 99.7% specificity against visual inspection of the RDTs. RDTScan helped CHWs capture high-quality RDT images within 18 seconds while enabling a better RDT workflow.