Korean J Parasitol.  2021 Apr;59(2):113-119. 10.3347/kjp.2021.59.2.113.

Performance Evaluation of Biozentech Malaria Scanner in Plasmodium knowlesi and P. falciparum as a New Diagnostic Tool

Affiliations
  • 1Department of Medical Environmental Biology and Tropical Medicine, School of Medicine, Kangwon National University, Chuncheon 24341, Korea
  • 2Department of Laboratory Medicine, Korea University College of Medicine, Seoul 08308, Korea
  • 3Department of Obstetrics and Gynecology, Kangwon National University Hospital, Kangwon National University School of Medicine, Chuncheon 24341, Korea
  • 4Department of Physiology, School of Medicine, Kangwon National University, Chuncheon 24341, Korea
  • 5Department of Anatomy and Cell Biology, School of Medicine, Kangwon National University, Chuncheon 24341, Korea

Abstract

The computer vision diagnostic approach currently generates several malaria diagnostic tools. It enhances the accessible and straightforward diagnostics that necessary for clinics and health centers in malaria-endemic areas. A new computer malaria diagnostics tool called the malaria scanner was used to investigate living malaria parasites with easy sample preparation, fast and user-friendly. The cultured Plasmodium parasites were used to confirm the sensitivity of this technique then compared to fluorescence-activated cell sorting (FACS) analysis and light microscopic examination. The measured percentage of parasitemia by the malaria scanner revealed higher precision than microscopy and was similar to FACS. The coefficients of variation of this technique were 1.2-6.7% for Plasmodium knowlesi and 0.3-4.8% for P. falciparum. It allowed determining parasitemia levels of 0.1% or higher, with coefficient of variation smaller than 10%. In terms of the precision range of parasitemia, both high and low ranges showed similar precision results. Pearson’s correlation test was used to evaluate the correlation data coming from all methods. A strong correlation of measured parasitemia (r2=0.99, P<0.05) was observed between each method. The parasitemia analysis using this new diagnostic tool needs technical improvement, particularly in the differentiation of malaria species.

Keyword

Plasmodium knowlesi; P. falciparum; parasitemia; malaria; diagnosis; computer vision
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