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Research Article

Application of Machine Learning in a Rodent Malaria Model for Rapid, Accurate, and Consistent Parasite Counts

Sean Yanik Department of Molecular Microbiology and Immunology, Johns Hopkins School of Public Health, Baltimore, Maryland;
Malaria Research Institute, Johns Hopkins School of Public Health, Baltimore, Maryland;

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Hang Yu National Library of Medicine, National Institutes of Health, Bethesda, Maryland;

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Nattawat Chaiyawong Department of Molecular Microbiology and Immunology, Johns Hopkins School of Public Health, Baltimore, Maryland;
Malaria Research Institute, Johns Hopkins School of Public Health, Baltimore, Maryland;

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Opeoluwa Adewale-Fasoro Department of Molecular Microbiology and Immunology, Johns Hopkins School of Public Health, Baltimore, Maryland;
Malaria Research Institute, Johns Hopkins School of Public Health, Baltimore, Maryland;

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Luciana Ribeiro Dinis Department of Molecular Microbiology and Immunology, Johns Hopkins School of Public Health, Baltimore, Maryland;
Malaria Research Institute, Johns Hopkins School of Public Health, Baltimore, Maryland;

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Ravi Kumar Narayanasamy Department of Molecular Microbiology and Immunology, Johns Hopkins School of Public Health, Baltimore, Maryland;
Malaria Research Institute, Johns Hopkins School of Public Health, Baltimore, Maryland;

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Elizabeth C. Lee Department of Molecular Microbiology and Immunology, Johns Hopkins School of Public Health, Baltimore, Maryland;
Malaria Research Institute, Johns Hopkins School of Public Health, Baltimore, Maryland;

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Ariel Lubonja Department of Computer Science, Johns Hopkins Whiting School of Engineering, Baltimore, Maryland

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Bowen Li Department of Computer Science, Johns Hopkins Whiting School of Engineering, Baltimore, Maryland

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Stefan Jaeger National Library of Medicine, National Institutes of Health, Bethesda, Maryland;

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Prakash Srinivasan Department of Molecular Microbiology and Immunology, Johns Hopkins School of Public Health, Baltimore, Maryland;
Malaria Research Institute, Johns Hopkins School of Public Health, Baltimore, Maryland;

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DOI:
https://doi.org/10.4269/ajtmh.24-0135
Volume/Issue:
Ahead of Print
Open access
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Rodent malaria models serve as important preclinical antimalarial and vaccine testing tools. Evaluating treatment outcomes in these models often requires manually counting parasite-infected red blood cells (iRBCs), a time-consuming process, which can be inconsistent between individuals and laboratories. We have developed an easy-to-use machine learning (ML)-based software, Malaria Screener R, to expedite and standardize such studies by automating the counting of Plasmodium iRBCs in rodents. This software can process Giemsa-stained blood smear images captured by any camera-equipped microscope. It features an intuitive graphical user interface that facilitates image processing and visualization of the results. The software has been developed as a desktop application that processes images on standard Windows and MacOS computers. A previous ML model created by the authors designed to count Plasmodium falciparum-infected human RBCs did not perform well counting Plasmodium-infected mouse RBCs. We leveraged that model by loading the pretrained weights and training the algorithm with newly collected data to target Plasmodium yoelii- and Plasmodium berghei-infected mouse RBCs. This new model reliably measured both P. yoelii and P. berghei parasitemia (R2 = 0.9916). Additional rounds of training data to incorporate variances due to length of Giemsa staining and type of microscopes, etc., have produced a generalizable model, meeting WHO competency level 1 for the subcategory of parasite counting using independent microscopes. Reliable, automated analyses of blood-stage parasitemia will facilitate rapid and consistent evaluation of novel vaccines and antimalarials across laboratories in an easily accessible in vivo malaria model.

Author Notes

Financial support: S. Yanik was supported by the NIH (grant no. T32 AI138953). N. Chaiyawong and O. Adewale-Fasoro were supported by Johns Hopkins Malaria Research Institute. E. C. Lee was supported by the NIH (grant no. T32AI138953). P. Srinivasan was supported in part by the NIH (grant no. R01AI155598) and the Johns Hopkins Malaria Research Institute.

Disclosures: The software is available for free download at https://lhncbc.nlm.nih.gov/LHC-research/LHC-projects/image-processing/malaria-project.html.

All animal experiments were approved by the Johns Hopkins Animal Care and Use Committee, under protocol MO22H289.

Current contact information: Sean Yanik, Nattawat Chaiyawong, Opeoluwa Adewale-Fasoro, Luciana Ribeiro Dinis, Ravi Kumar Narayanasamy, Elizabeth C. Lee, and Prakash Srinivasan, Department of Molecular Microbiology and Immunology and Malaria Research Institute, Johns Hopkins School of Public Health, Baltimore, MD, E-mails: syanik1@jh.edu, nchaiya1@jhmi.edu, oadewal1@jhmi.edu, ldinis1@jhmi.edu, rnaray13@jh.edu, elee171@jhu.edu, and psriniv3@jhu.edu. Hang Yu and Stefan Jaeger, National Library of Medicine, National Institutes of Health, Bethesda, MD, E-mails: hang.yu@nih.gov and stefan.jaeger@nih.gov. Ariel Lubonja and Bowen Li, Department of Computer Science, Johns Hopkins Whiting School of Engineering, Baltimore, MD, E-mails: alubonj1@jhu.edu and lbowen5@jhu.edu.

Address correspondence to Prakash Srinivasan, Department of Molecular Microbiology and Immunology and Malaria Research Institute, 615 N. Wolfe St, Rm E5628, Johns Hopkins School of Public Health, Baltimore, MD, 21025, E-mail: psriniv3@jhu.edu or Stefan Jaeger, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD 20894, E-mail: stefan.jaeger@nih.gov
Copyright:
© The author(s) 2024
Received:
27 Feb 2024
|
Accepted:
04 Jun 2024
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Published Online:
10 Sep 2024
Cover The American Journal of Tropical Medicine and Hygiene
The American Journal of Tropical Medicine and Hygiene
Print ISSN:
0002-9637
Online ISSN:
1476-1645
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