Multi-centric Field Evaluation Of A Digital Malaria Microscopy Device Based On Machine-Learning: EasyScan GO – A Preliminary Analysis

Infectious Diseases Data Observatory & WorldWide Antimalarial Resistance Network, UK, Infectious Diseases Data Observatory & WorldWide Antimalarial Resistance Network, Asia-Pacific Regional Centre, et al.

In this work, a team assessed the performance of the EasyScanGO, a microscopy device employing machine-learning-based image analysis to detect malaria parasites. Microscopic examination of Giemsa-stained blood lms remains the reference standard for laboratory confirmation of malaria but is undermined by difficulties in ensuring high-quality manual reading and inter-reader reliability. An automated parasite detection tool, such as the EasyScanGO, in combination with quantification, may address this issue. This prospective study was conducted during 2018 and 2019 across 10 sites in 10 countries from Africa, Asia, and South America.

Read more about the study on ClinicalTrials.Gov here

By The Motic Team

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