Application of machine learning in a rodent malaria model for rapid, accurate, and consistent parasite counts

Abstract excerpt

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 labs. We have developed an easy-to-use machine learning (ML)-based software, Malaria Screener R, to expedite and standardize s...

Topics

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• Aquaculture disease management and microbiota
• Computational Drug Discovery Methods
• Hepatitis C virus research
• interferon and immune responses
• Malaria Research and Control
• Mosquito-borne diseases and control

Identifiers and source

Literature Corpus work

f4b0808f-7d2c-5204-974f-4417cc60178f

DOI

10.1101/2024.06.05.597554