A Novel Deep Learning-Based Point-of-Care Diagnostic Method for Detecting Plasmodium falciparum with Fluorescence Digital Microscopy

Light microscopy assessment of blood smears to detect Plasmodium parasites remains the diagnostic gold standard and allows detection and quantification of the various Plasmodium species while also being more sensitive than rapid diagnostic tests. Samples were visually evaluated by two researchers, who independently reviewed the digital samples on an LCD computer monitor and counted all visible Plasmodium parasites in the images. Parasites were manually annotated on the slide-management platform and the annotations served as ground truth for the digital image analysis. As digital microscopy is currently limited mainly to laboratories with access to high-end digitization equipment, this method warrants further investigation as a potential novel platform for AI-based, digital malaria microscopy at the POC. The method can facilitate microscopy diagnostics in field settings and offer the benefits that digital and automated microscopy is associated with, e.g., more objective and reproducible results, potentially reduced time for needed for sample analysis.