Performance of a fully‐automated system on a WHO malaria microscopy evaluation slide set

Charles B. Delahunt, Matthew P Horning

Abstract:

Background: Manual microscopy remains a widely-used tool for malaria diagnosis and clinical studies, but it has
inconsistent quality in the field due to variability in training and field practices. Automated diagnostic systems based
on machine learning hold promise to improve quality and reproducibility of field microscopy. The World Health
Organization (WHO) has designed a 55-slide set (WHO 55) for their External Competence Assessment of Malaria
Microscopists (ECAMM) programme, which can also serve as a valuable benchmark for automated systems. The performance
of a fully-automated malaria diagnostic system, EasyScan GO, on a WHO 55 slide set was evaluated.
Methods: The WHO 55 slide set is designed to evaluate microscopist competence in three areas of malaria diagnosis
using Giemsa-stained blood films, focused on crucial field needs: malaria parasite detection, malaria parasite species
identification (ID), and malaria parasite quantitation. The EasyScan GO is a fully-automated system that combines
scanning of Giemsa-stained blood films with assessment algorithms to deliver malaria diagnoses. This system was
tested on a WHO 55 slide set.
Results: The EasyScan GO achieved 94.3 % detection accuracy, 82.9 % species ID accuracy, and 50 % quantitation
accuracy, corresponding to WHO microscopy competence Levels 1, 2, and 1, respectively. This is, to our knowledge,
the best performance of a fully-automated system on a WHO 55 set.
Conclusions: EasyScan GO’s expert ratings in detection and quantitation on the WHO 55 slide set point towards its
potential value in drug efficacy use-cases, as well as in some case management situations with less stringent species
ID needs. Improved runtime may enable use in general case management settings.