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

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...

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Detalles Bibliográficos
Autores principales: Das, Debashish, Vongpromek, Ranitha, Assawariyathipat, Thanawat, Srinamon, Ketsanee, Kennon, Kalynn, Stepniewska, Kasia, Ghose, Aniruddha, Sayeed, Abdullah Abu, Faiz, M. Abul, Netto, Rebeca Linhares Abreu, Siqueira, Andre, Yerbanga, Serge R., Ouédraogo, Jean Bosco, Callery, James J., Peto, Thomas J., Tripura, Rupam, Koukouikila-Koussounda, Felix, Ntoumi, Francine, Ong’echa, John Michael, Ogutu, Bernhards, Ghimire, Prakash, Marfurt, Jutta, Ley, Benedikt, Seck, Amadou, Ndiaye, Magatte, Moodley, Bhavani, Sun, Lisa Ming, Archasuksan, Laypaw, Proux, Stephane, Nsobya, Sam L., Rosenthal, Philip J., Horning, Matthew P., McGuire, Shawn K., Mehanian, Courosh, Burkot, Stephen, Delahunt, Charles B., Bachman, Christine, Price, Ric N., Dondorp, Arjen M., Chappuis, François, Guérin, Philippe J., Dhorda, Mehul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9004086/
https://www.ncbi.nlm.nih.gov/pubmed/35413904
http://dx.doi.org/10.1186/s12936-022-04146-1

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