Evaluation of an automated microscope using machine learning for the detection of malaria in travelers returned to the UK

Introduction 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. Methods 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. Results 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 Plasmodium falciparum ( Pf ) and 12 as non- Pf . The EasyScan GO correctly reported Pf for 86 of the 87 Pf samples and non- Pf for 11 of 12 non- Pf samples. However, it failed to distinguish between non- Pf species, reporting all as P. vivax . The EasyScan GO calculated parasite densities were within +/-25% of light microscopy densities for 33% of samples between 200 and 2000 p/µL, falling short of WHO level 1 (expert) manual microscopy competency (50% of samples should be within +/-25% of the true parasitemia). Discussion 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 Pf and non- Pf species. Performance at low parasite densities, distinguishing between non- Pf species and accurate quantitation of parasitemia require further development and evaluation.