A novel capillary-driven dual-mode imaging flow cytometry system for malaria parasite detection and quantification.

Malaria remains a significant global health burden, particularly in tropical and subtropical regions with limited resources. Accurate quantification of parasitemia is critical for effective treatment and disease management. While microscopy is the gold standard in diagnostics, it is labour-intensive and highly dependent on operator expertise. Here, we present a capillary-driven image flow cytometer with a cost comparable to microscopy-based diagnostics. This system integrates brightfield and fluorescence imaging into a single image, encoding morphological information in the red channel and fluorescence information in the green channel of a Red Green Blue (RGB) image. The system simplifies malaria diagnosis and quantification into straightforward steps, involving the staining of blood with SYBR Green I before loading it into a microfluidic chip. The chip is then placed under a microscope with both brightfield and fluorescence illumination. Automated image acquisition and processing enable the generation of diagnostic results without user intervention. We evaluated the robustness and repeatability of the system against a commercial flow cytometer using samples with parasitemia levels of 1.45% and 5.98%. The platform successfully diagnoses malaria and quantifies parasitemia with a relative error of less than 10% compared to standard flow cytometry on the cultured Plasmodium falciparum. The platform additionally provides information about the area fraction of red blood cell (RBC) occupied by the parasite, which enables insight into the stage of the parasites or the number of parasites per cell. While the current proof-of-concept validation is limited to cultured samples at moderate-to-high parasitemia levels, the results highlight the feasibility of such integrated systems. The future work will prioritise the replacement of polydimethylsiloxane (PDMS) chips with poly(methyl methacrylate) (PMMA) chips and integration of on-board staining, as well as validation of the platform on patient-derived clinical samples over clinically relevant parasitemia levels in field settings.