Evaluation of an Automated Microscope Using Machine Learning for the Detection of Malaria in Travelers Returned to the UK

Hospital for Tropical Diseases, London, UK, Global Health Laboratories, Bellevue, WA, U.S.,  London School of Hygiene and Tropical Medicine, London, UK, Global Health Laboratories, Bellvue, WA, Creative Creek LLC, Camano Island, WA, U.S.

In this work, a team evaluated EasyScan GO to determine if it could detect, quantitate, and identify malaria parasites present in Giemsa-stained blood lms with consistency and accuracy compared to trained microscopists. The EasyScanGO is an automated scanning microscope combined with machine learning to detect parasites in Giemsa-stained blood lms. An automated microscopy tool could be an effective aid in areas with limited access to skilled microscopists, where malaria is seen rarely, and in studies where consistency in microscopy between sites is essential.

By The Motic Team

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