Publication: Performance of a fully‐automated system on a WHO malaria microscopy evaluation slide set
Issued Date
2021-12-01
Resource Type
ISSN
14752875
Other identifier(s)
2-s2.0-85101771435
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Mahidol University
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SCOPUS
Bibliographic Citation
Malaria Journal. Vol.20, No.1 (2021)
Suggested Citation
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 Performance of a fully‐automated system on a WHO malaria microscopy evaluation slide set. Malaria Journal. Vol.20, No.1 (2021). doi:10.1186/s12936-021-03631-3 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/77185
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Title
Performance of a fully‐automated system on a WHO malaria microscopy evaluation slide set
Author(s)
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
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 Contributor(s)
Mahidol Oxford Tropical Medicine Research Unit
Gokila
Universidad Peruana Cayetano Heredia
Kenya Medical Research Institute
Amref Health Africa
London School of Hygiene & Tropical Medicine
Centers for Disease Control and Prevention
James Cook University
University of Washington
Mahidol University
University of Lagos
Arizona State University
Australian Defence Force Malaria and Infectious Disease Institute
Independent Consultant
Asia Regional Centre
Defence Services Medical Academy
Creative Creek, LLC
Intellectual Ventures Global Good Fund
Intellectual Ventures
Gokila
Universidad Peruana Cayetano Heredia
Kenya Medical Research Institute
Amref Health Africa
London School of Hygiene & Tropical Medicine
Centers for Disease Control and Prevention
James Cook University
University of Washington
Mahidol University
University of Lagos
Arizona State University
Australian Defence Force Malaria and Infectious Disease Institute
Independent Consultant
Asia Regional Centre
Defence Services Medical Academy
Creative Creek, LLC
Intellectual Ventures Global Good Fund
Intellectual Ventures
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 reproducibility of field microscopy. The World Health Organization (WHO) has designed a 55-slide set (WHO 55) for their External Competence Assessment of Malaria Microscopists (ECAMM) programme, which can also serve as a valuable benchmark for automated systems. The performance of a fully-automated malaria diagnostic system, EasyScan GO, on a WHO 55 slide set was evaluated. Methods: The WHO 55 slide set is designed to evaluate microscopist competence in three areas of malaria diagnosis using Giemsa-stained blood films, focused on crucial field needs: malaria parasite detection, malaria parasite species identification (ID), and malaria parasite quantitation. The EasyScan GO is a fully-automated system that combines scanning of Giemsa-stained blood films with assessment algorithms to deliver malaria diagnoses. This system was tested on a WHO 55 slide set. Results: The EasyScan GO achieved 94.3 % detection accuracy, 82.9 % species ID accuracy, and 50 % quantitation accuracy, corresponding to WHO microscopy competence Levels 1, 2, and 1, respectively. This is, to our knowledge, the best performance of a fully-automated system on a WHO 55 set. Conclusions: EasyScan GO’s expert ratings in detection and quantitation on the WHO 55 slide set point towards its potential value in drug efficacy use-cases, as well as in some case management situations with less stringent species ID needs. Improved runtime may enable use in general case management settings.