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

<strong>Background<br></strong> 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...

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Main Authors:	Das, D, Vongpromek, R, Assawariyathipat, T, Srinamon, K, Kennon, K, Stepniewska, K, Ghose, A, Sayeed, AA, Faiz, MA, Netto, RLA, Siqueira, A, Yerbanga, SR, Ouédraogo, JB, Callery, JJ, Peto, TJ, Tripura, R, Koukouikila-Koussounda, F, Ntoumi, F, Ong'echa, JM, Ogutu, B, Ghimire, P, Marfurt, J, Ley, B, Seck, A, Ndiaye, M, Moodley, B, Sun, LM, Archasuksan, L, Proux, S, Nsobya, SL, Rosenthal, PJ, Horning, MP, McGuire, SK, Mehanian, C, Burkot, S, Delahunt, CB, Bachman, C, Price, RN, Dondorp, AM, Chappuis, F, Guérin, PJ, Dhorda, M
Format:	Journal article
Language:	English
Published:	BioMed Central 2022

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

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