Light microscopy remains a standard method for detection of malaria parasites in clinical cases but training to expert level requires considerable time. Moreover, excessive workflow causes fatigue and can impact performance. An automated microscopy tool could aid in clinics with limited access to highly skilled microscopists, where case numbers are excessive, or in multi-site studies where consistency is essential. The EasyScan GO is an automated scanning microscope combined with machine learning software designed to detect malaria parasites in field-prepared Giemsa-stained blood films. This study evaluates the ability of the EasyScan GO to detect, quantify and identify the species of parasite present in blood films compared with expert light microscopy.
Travelers returning to the UK and testing positive for malaria were screened for eligibility and enrolled. Blood samples from enrolled participants were used to make Giemsa-stained smears assessed by expert light microscopy and the EasyScan GO to determine parasite density and species. Blood samples were also assessed by PCR to confirm parasite density and species present and resolve discrepancy between manual microscopy and the EasyScan GO.
When compared to light microscopy, the EasyScan GO exhibited a sensitivity of 88% (95% CI: 80-93%) and a specificity of 89% (95% CI: 87-91%). Of the 99 samples labelled positive by both, manual microscopy identified 87 as
This study shows that the EasyScan GO can be proficient in detecting malaria parasites in Giemsa-stained blood films relative to expert light microscopy and accurately distinguish between