Microscopy is essential for malaria diagnosis, parasite species differentiation, and density estimation. A systematic literature review was performed to explore how microscopy methods were reported in published malaria studies (2013-2017). Consequently, an evidence-based checklist for reporting microscopy procedures has been proposed.

Malaria microscopy is undermined by difficulties in ensuring high-quality manual reading and inter-reader reliability. A multi-centre, observational study was conducted in 2018-19 to assess the diagnostic performance of EasyScan Go, a machine-learning-based microscope. Of 2,250 blood films evaluated, the diagnostic sensitivity of EasyScan Go was 91.1% (95%CI 88.9-92.7), and specificity 75.6% (95%CI 73.1-78.0). Species were identified accurately in 93% of P. falciparum (kappa=0.76, 95%CI 0.69-0.83), and in 92% of P. vivax samples (kappa=0.73, 95%CI 0.66-0.80). Parasite density estimates by EasyScan Go were within +/- 25% of the microscopic reference counts in 23% of slides. Further software improvement is required to improve sensitivity at low parasitaemia and parasite density estimations.