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

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.

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

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 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 Mac OS computers. A previous ML model created by the authors designed to count P. falciparum -infected human RBCs did not perform well counting Plasmodium -infected mouse RBCs. We leveraged that model by loading the pre-trained weights and training the algorithm with newly collected data to target P. yoelii and P. berghei mouse iRBCs. This new model reliably measured both P. yoelii and P. berghei parasitemia (R 2 = 0.9916). Additional rounds of training data to incorporate variances due to length of Giemsa staining, microscopes etc, have produced a generalizable model, meeting WHO Competency Level 1 for the sub-category 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 labs in an easily accessible in vivo malaria model.