Mobile phone imaging and cloud-based analysis for standardized malaria detection and reporting

摘要

Rapid diagnostic tests (RDTs) have been widely deployed in low-resource settings. These tests are typically read by visual inspection, and accurate record keeping and data aggregation remains a substantial challenge. A successful malaria elimination campaign will require new strategies that maximize the sensitivity of RDTs, reduce user error, and integrate results reporting tools. In this report, an unmodified mobile phone was used to photograph RDTs, which were subsequently uploaded into a globally accessible database, REDCap, and then analyzed three ways: with an automated image processing program, visual inspection, and a commercial lateral flow reader. The mobile phone image processing detected 20.6 malaria parasites/microliter of blood, compared to the commercial lateral flow reader which detected 64.4 parasites/microliter. Experienced observers visually identified positive malaria cases at 12.5 parasites/microliter, but encountered reporting errors and false negatives. Visual interpretation by inexperienced users resulted in only an 80.2% true negative rate, with substantial disagreement in the lower parasitemia range. We have demonstrated that combining a globally accessible database, such as REDCap, with mobile phone based imaging of RDTs provides objective, secure, automated, data collection and result reporting. This simple combination of existing technologies would appear to be an attractive tool for malaria elimination campaigns.