Field evaluation of the diagnostic performance of EasyScan GO: a digital malaria microscopy device based on machine-learning.

ABSTRACT:

Background

Microscopic examination of Giemsa-stained blood films remains the reference standard for malaria parasite detection and quantification, but is undermined by difficulties in ensuring high-quality manual reading and inter-reader reliability. Automated parasite detection and quantification may address this issue.

Methods

A multi-centre, observational study was conducted during 2018 and 2019 at 11 sites to assess the performance of the EasyScan Go, a microscopy device employing machine-learning-based image analysis. 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 th

SUBMITTER: Das D 

PROVIDER: S-EPMC9004086 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature