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

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

Abstract 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 rep...

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Bibliographic Details
Main Authors: Matthew P. Horning, Charles B. Delahunt, Christine M. Bachman, Jennifer Luchavez, Christian Luna, Liming Hu, Mayoore S. Jaiswal, Clay M. Thompson, Sourabh Kulhare, Samantha Janko, Benjamin K. Wilson, Travis Ostbye, Martha Mehanian, Roman Gebrehiwot, Grace Yun, David Bell, Stephane Proux, Jane Y. Carter, Wellington Oyibo, Dionicia Gamboa, Mehul Dhorda, Ranitha Vongpromek, Peter L. Chiodini, Bernhards Ogutu, Earl G. Long, Kyaw Tun, Thomas R. Burkot, Ken Lilley, Courosh Mehanian
Format: Article
Language:English
Published: BMC 2021-02-01
Series:Malaria Journal
Subjects:
Malaria
Automated diagnosis
Machine learning
Microscopy
WHO
Online Access:https://doi.org/10.1186/s12936-021-03631-3

Internet

https://doi.org/10.1186/s12936-021-03631-3

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