Performance of a Fully-automated System on a WHO Malaria Microscopy Evaluation Slide Set

Abstract

Background: Manual microscopy remains a widely-used tool for malaria diagnosis and clinical studies, but it has inconsistent quality in the feld due to variability in training and feld practices. Automated diagnostic systems based on machine learning hold promise to improve quality and reproducibility of feld 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 per￾formance 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 flms, focused on crucial feld needs: malaria parasite detection, malaria parasite species identifcation (ID), and malaria parasite quantitation. The EasyScan GO is a fully-automated system that combines scanning of Giemsa-stained blood flms 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 efcacy 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.