Field evaluation of the diagnostic performance of EasyScan GO: a digital malaria microscopy device based on machine-learning

• Main Authors: Debashish Das, Ranitha Vongpromek, Thanawat Assawariyathipat, Ketsanee Srinamon, Kalynn Kennon, Kasia Stepniewska, Aniruddha Ghose, Abdullah Abu Sayeed, M. Abul Faiz, Rebeca Linhares Abreu Netto, Andre Siqueira, Serge R. Yerbanga, Jean Bosco Ouédraogo, James J. Callery, Thomas J. Peto, Rupam Tripura, Felix Koukouikila-Koussounda, Francine Ntoumi, John Michael Ong’echa, Bernhards Ogutu, Prakash Ghimire, Jutta Marfurt, Benedikt Ley, Amadou Seck, Magatte Ndiaye, Bhavani Moodley, Lisa Ming Sun, Laypaw Archasuksan, Stephane Proux, Sam L. Nsobya, Philip J. Rosenthal, Matthew P. Horning, Shawn K. McGuire, Courosh Mehanian, Stephen Burkot, Charles B. Delahunt, Christine Bachman, Ric N. Price, Arjen M. Dondorp, François Chappuis, Philippe J. Guérin, Mehul Dhorda
• Other Authors: Faculty of Tropical Medicine, Mahidol University
• Format: Article
• Published: 2022
• Subjects: Immunology and Microbiology; Medicine
• Online Access: https://repository.li.mahidol.ac.th/handle/123456789/74075
• Institution: Mahidol University

Background: Microscopic examination of Giemsa-stained blood films remains the reference standard for malaria parasite detection and quantification, but is undermined by difficulties in ensuring high-quality manual reading and inter-reader reliability. Automated parasite detection and quantification...