An automated microscopic Malaria parasite detection system using digital image analysis

Jung Yoon, Woong Sik Jang, Jeonghun Nam, Do Cic Mihn, Chae Seung Lim

Research output: Contribution to journalArticlepeer-review

Abstract

Rapid diagnosis and parasitemia measurement is crucial for management of malaria. Microscopic examination of peripheral blood (PB) smears is the gold standard for malaria detection. However, this method is labor-intensive. Here, we aimed to develop a completely automated microscopic system for malaria detection and parasitemia measurement. The automated system comprises a microscope, plastic chip, fluorescent dye, and an image analysis program. Analytical performance was evaluated regarding linearity, precision, and limit of detection and was compared with that of conventional microscopic PB smear examination and flow cytometry. The automated microscopic malaria parasite detection system showed a high degree of linearity for Plasmodium falciparum culture (R2 = 0.958, p = 0.005) and Plasmodium vivax infected samples (R2 = 0.931, p = 0.008). Precision was defined as the %CV of the assay results at each level of parasitemia and the %CV value for our system was lower than that for microscopic examination for all densities of parasitemia. The limit of detection analysis showed 95% probability for parasite detection was 0.00066112%, and a high correlation was observed among all three methods. The sensitivity and specificity of the system was both 100% (n = 21/21) and 100% (n = 50/50), respectively, and the system correctly identified all P. vivax and P. falciparum samples. The automated microscopic malaria parasite detection system offers several advantages over conventional microscopy for rapid diagnosis and parasite density monitoring of malaria.

Original languageEnglish
Article number527
JournalDiagnostics
Volume11
Issue number3
DOIs
Publication statusPublished - 2021 Mar

Keywords

  • Automation
  • Malaria
  • Microscopy
  • P. falciparum
  • P. vivax
  • Parasitemia

ASJC Scopus subject areas

  • Clinical Biochemistry

Fingerprint

Dive into the research topics of 'An automated microscopic Malaria parasite detection system using digital image analysis'. Together they form a unique fingerprint.

Cite this