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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Detalles Bibliográficos
Autores principales: 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: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7905596/
https://www.ncbi.nlm.nih.gov/pubmed/33632222
http://dx.doi.org/10.1186/s12936-021-03631-3

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7905596/
https://www.ncbi.nlm.nih.gov/pubmed/33632222
http://dx.doi.org/10.1186/s12936-021-03631-3

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