Application of artificial intelligence for quantifying Plasmodium berghei in blood samples of infected mice.

BACKGROUND

OBJECTIVES

In malaria infection, quantifying blood parasitemia is a critical step for evaluating the severity of the disease. This has generally been conducted manually, and thus its accuracy depends on technician expertise. There is an urgent need for an automated technique to overcome manual errors. The aim of this study was to find an alternative method for counting malaria blood parasitemia.

METHODS

This study evaluated the accuracy of automated counting using QuPath compared to manual counting. GraphPad Kappa evaluated agreement between high and low parasitemia in both counting methods using Cohen's test.

RESULTS

QuPath was revealed to be a promising method that has fair agreement and no statistically significant differences compared to manual counting.

INTERPRETATION

CONCLUSION

Automated quantification is suggested to be a time and effort-saving technique and therefore is a worthwhile alternative to manual counting.