Pursuing the elusive footsteps of malaria in peripheral blood smears utilizing artificial intelligence.

For over a century, the need to identify malaria in the peripheral blood has been the driving force behind the development of fundamental clinical microscopy techniques. In the study by Moysis et al., artificial intelligence-based model was utilized to identify and provide quantitative morphological characteristics of red blood cells typical to severe malaria anaemia, irrespective to the actual presence of visible parasites. Commentary on: Moysis et al. Leveraging deep learning for detecting red blood cell morphological changes in blood films from children with severe malaria anaemia. Br J Haematol 2024;205:699-710.