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Microscopic examination of Giemsa-stained blood films is key to quantifying and detecting malaria parasites but there can be difficulties in ensuring both a high-quality manual reading and inter-reader reliability. The EasyScan GO was developed as a potential solution to this, a microscopy device using machine-learning-based image analysis for automated parasite detection and quantification.

Three researchers using microscopes to study malaria parasites © Credit Mehul Dhorda

Recent WWARN research details the multi-centre, observational study  conducted to assess the performance of the EasyScan GO at 11 sites worldwide during 2018 and 2019. Sensitivity, specificity, accuracy of species detection and parasite density estimation were assessed with expert microscopy as the reference. Intra- and inter-device reliability of the device was also evaluated by comparing results from repeat reads on the same and two different devices.

The EasyScan GO met the WHO-TDR Research Malaria Microscopy competence Level 2 criteria in parasite detection and species identification accuracy; and Level 4 in terms of parasite quantification and false positive rates. All performance parameters were significantly affected by slide quality. Further software improvement is required to improve sensitivity at low parasitaemia and parasite density estimations.

The full story is available on the WWARN website

Read the publication 'Field evaluation of the diagnostic performance of EasyScan GO: a digital malaria microscopy device based on machine-learning' on the Malaria Journal website

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