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Malaria Detection Accelerated: Combing a High-Throughput NanoZoomer Platform with a ParasiteMacro Algorithm
- Source :
- Pathogens, Vol 11, Iss 10, p 1182 (2022)
- Publication Year :
- 2022
- Publisher :
- MDPI AG, 2022.
-
Abstract
- Eradication of malaria, a mosquito-borne parasitic disease that hijacks human red blood cells, is a global priority. Microscopy remains the gold standard hallmark for diagnosis and estimation of parasitemia for malaria, to date. However, this approach is time-consuming and requires much expertise especially in malaria-endemic countries or in areas with low-density malaria infection. Thus, there is a need for accurate malaria diagnosis/parasitemia estimation with standardized, fast, and more reliable methods. To this end, we performed a proof-of-concept study using the automated imaging (NanoZoomer) platform to detect the malarial parasite in infected blood. The approach can be used as a steppingstone for malaria diagnosis and parasitemia estimation. Additionally, we created an algorithm (ParasiteMacro) compatible with free online imaging software (ImageJ) that can be used with low magnification objectives (e.g., 5×, 10×, and 20×) both in the NanoZoomer and routine microscope. The novel approach to estimate malarial parasitemia based on modern technologies compared to manual light microscopy demonstrated 100% sensitivity, 87% specificity, a 100% negative predictive value (NPV) and a 93% positive predictive value (PPV). The manual and automated malaria counts showed a good Pearson correlation for low- (R2 = 0.9377, r = 0.9683 and p < 0.0001) as well as high- parasitemia (R2 = 0.8170, r = 0.9044 and p < 0.0001) with low estimation errors. Our robust strategy that identifies and quantifies malaria can play a pivotal role in disease control strategies.
- Subjects :
- NanoZoomer
ParasiteMacro
algorithm
microscopy
malaria
Plasmodium falciparum
Medicine
Subjects
Details
- Language :
- English
- ISSN :
- 20760817
- Volume :
- 11
- Issue :
- 10
- Database :
- Directory of Open Access Journals
- Journal :
- Pathogens
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.83546b5d36d541bd9a66a810fe91f2c5
- Document Type :
- article
- Full Text :
- https://doi.org/10.3390/pathogens11101182