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

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.

Requirements for diagnosis of malaria at different levels of the laboratory network in Africa.

The rapid increase of resistance to cheap, reliable antimalarials, the increasing cost of effective drugs, and the low specificity of clinical diagnosis has increased the need for more reliable diagnostic methods for malaria. The most commonly used and most reliable remains microscopic examination of stained blood smears, but this technique requires skilled personnel, precision instruments, and ideally a source of electricity. Microscopy has the advantage of enabling the examiner to identify the species, stage, and density of an infection.

Mosquitoborne infections after Hurricane Jeanne, Haiti, 2004.

After Hurricane Jeanne in September 2004, surveillance for mosquitoborne diseases in Gonaïves, Haiti, identified 3 patients with malaria, 2 with acute dengue infections, and 2 with acute West Nile virus infections among 116 febrile patients. These are the first reported human West Nile virus infections on the island of Hispaniola.