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

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 quantif...

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Main Authors: Debashish Das, Ranitha Vongpromek, Thanawat Assawariyathipat, Ketsanee Srinamon, Kalynn Kennon, Kasia Stepniewska, Aniruddha Ghose, Abdullah Abu Sayeed, M. Abul Faiz, Rebeca Linhares Abreu Netto, Andre Siqueira, Serge R. Yerbanga, Jean Bosco Ouédraogo, James J. Callery, Thomas J. Peto, Rupam Tripura, Felix Koukouikila-Koussounda, Francine Ntoumi, John Michael Ong’echa, Bernhards Ogutu, Prakash Ghimire, Jutta Marfurt, Benedikt Ley, Amadou Seck, Magatte Ndiaye, Bhavani Moodley, Lisa Ming Sun, Laypaw Archasuksan, Stephane Proux, Sam L. Nsobya, Philip J. Rosenthal, Matthew P. Horning, Shawn K. McGuire, Courosh Mehanian, Stephen Burkot, Charles B. Delahunt, Christine Bachman, Ric N. Price, Arjen M. Dondorp, François Chappuis, Philippe J. Guérin, Mehul Dhorda
Format: Article
Language:English
Published: BMC 2022-04-01
Series:Malaria Journal
Subjects:
Malaria
Light microscopy
Digital microscopy
Artificial intelligence
Diagnostic accuracy
Machine-learning
Online Access:https://doi.org/10.1186/s12936-022-04146-1

Internet

https://doi.org/10.1186/s12936-022-04146-1

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