Assessment of Expert-Level Automated Detection of Plasmodium falciparum in Digitized Thin Blood Smear Images.

ABSTRACT: Importance:Decades of effort have been devoted to establishing an automated microscopic diagnosis of malaria, but there are challenges in achieving expert-level performance in real-world clinical settings because publicly available annotated data for benchmark and validation are required. Objective:To assess an expert-level malaria detection algorithm using a publicly available benchmark image data set. Design, Setting, and Participants:In this diagnostic study, clinically validated malaria image data sets, the Taiwan Images for Malaria Eradication (TIME), were created by digitizing thin blood smears acquired from patients with malaria selected from the biobank of the Taiwan Centers for Disease Control from January 1, 2003, to December 31, 2018. These smear images were annotated by 4 clini

SUBMITTER: Kuo PC 

PROVIDER: S-EPMC7049085 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature