Review highlights artificial intelligence and deep learning tools for malaria parasite detection in blood smears, suggesting strong potential for diagnostics in resource-limited areas.
Key Points
Artificial intelligence tools effectively identify and quantify plasmodium parasites across blood smears, delivering automated diagnostic support for global disease management.
Literature review examines deep learning architectures and convolutional neural networks applied to digital microscopy images across resource-constrained healthcare settings.
Highlights ongoing implementation hurdles including dataset scarcity and algorithm stability, while supporting scalable disease surveillance across underserved tropical regions.