Performance of a fully‐automated system on a WHO malaria microscopy evaluation slide set

BACKGROUND: Manual microscopy remains a widely-used tool for malaria diagnosis and clinical studies, but it has inconsistent quality in the field due to variability in training and field practices. Automated diagnostic systems based on machine learning hold promise to improve quality and reproducibi...

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Detalhes bibliográficos
Publicado no:Malar J
Main Authors: Horning, Matthew P., Delahunt, Charles B., Bachman, Christine M., Luchavez, Jennifer, Luna, Christian, Hu, Liming, Jaiswal, Mayoore S., Thompson, Clay M., Kulhare, Sourabh, Janko, Samantha, Wilson, Benjamin K., Ostbye, Travis, Mehanian, Martha, Gebrehiwot, Roman, Yun, Grace, Bell, David, Proux, Stephane, Carter, Jane Y., Oyibo, Wellington, Gamboa, Dionicia, Dhorda, Mehul, Vongpromek, Ranitha, Chiodini, Peter L., Ogutu, Bernhards, Long, Earl G., Tun, Kyaw, Burkot, Thomas R., Lilley, Ken, Mehanian, Courosh
Formato: Artigo
Idioma:Inglês
Publicado em: BioMed Central 2021
Assuntos:
Research
Acesso em linha:https://ncbi.nlm.nih.gov/pmc/articles/PMC7905596/
https://ncbi.nlm.nih.gov/pubmed/33632222
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/s12936-021-03631-3
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