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

• 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
• Other Authors: Mahidol Oxford Tropical Medicine Research Unit
• Format: Article
• Published: 2022
• Subjects: Immunology and Microbiology; Medicine
• Online Access: https://repository.li.mahidol.ac.th/handle/123456789/77185
• Institution: Mahidol University

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...