Tackling Malaria Diagnosis in sub-Saharan Africa with Fast, Accurate and Scalable Robotic Automation, Computer Vision and Machine Learning (FASt-Mal)

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Malaria affect about 300 million people worldwide leading to around one million deaths each year. Up to eighty-five percent of the cases occur in sub-Saharan Africa with about 90% mortality in the under five years-of-age group due to severe malaria syndromes. Control of malaria remains a major public health issue in sub-Saharan Africa developing countries. A quarter of the global malaria cases and a third of malaria-attributable childhood deaths occur in the most populous country of Africa, Nigeria (160M inhabitants) and indicates the importance of the problem. Accurate malaria diagnosis relies on the recognition of clinical parameters and more importantly in the microscopic detection of malarial parasites, parasitised red-blood-cells in peripheral-blood films. Malaria parasite detection and counting by human-operated optical microscopy is the current "gold standard" and despite its major severe drawbacks, other non-microscopic methodologies have not been able to outperform it. Presumptive treatment for malaria (without microscopic confirmation) is wasteful of drugs and ineffective if the diagnosis was wrong, a drain on often precious health resources, fuels antimalarial resistance and have made control and elimination interventions unachievable. We aim to create and test in real-world conditions a fast, accurate and scalable malaria diagnosis system by replacing human-expert optical-microscopy with a robotic automated computer-expert system FASt-MalPrototype that assesses similar digital-optical-microscopy representations of the problem. The system aims to provide access to effective malaria diagnosis, a challenge that is faced by all developing countries where malaria is endemic.