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

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.

The global incidence of clinical malaria is estimated at about 300 million cases 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. Our proposal addresses a key problem primarily related to these large group of low- and low-to-middle income countries with large burden Global Health Challenges such a malaria. 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. Large all-year-round lethal malaria morbidity and mortality burden has hindered Ibadan wellbeing and economic development within the South-West Nigerian geopolitical region. Key strategies for malaria control has been to reduce mortality by rapid treatment with antimalarial drugs, but this has been stalled by the lack of scalable accurate diagnosis methods. Human-microscopic examination of blood smears remains the "gold standard" for malaria diagnosis and despite its major severe drawbacks, other non-microscopic methodologies have not been able to outperform it. Access to effective malaria diagnosis is a challenge faced by all developing countries where malaria is endemic. 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. This has prompted WHO, CDC and other Global Health organisations to emphasise the urgent need for tools to overcome the deficiencies of human-operated optical-microscopy malaria diagnosis. Our research aims to provide a novel solution for automated fast accurate scalable computational optical-microscopy identification of malaria parasites that will certainly underpin the design and development of future portable accurate and cost-effective malaria-detection devices. Our proposal has a clear path to immediate- and near-future impact outcomes across malaria endemic regions. The proposed timeline is to achieve the immediate-future impact outcome: the design and deployment of a cheaper and optimised robotic bench-top prototype at our primary beneficiary overseas partner at COMUI. Our Nigeria-UK team will carry-out a novel large-scale preclinical assessment of the validity of the automated computational system in real world clinical conditions. The COMUI University College Hospital Ibadan is a centre of academic excellence and attracts both wellbeing and ill people seeking affordable good quality healthcare. Our population footprint has allowed us to execute activities to engage users from different socio-economic backgrounds. Our engagement with primary users is extremely high given to the all-year-round burden of malaria at all ages of the population of the Ibadan metropolis. All inhabitants are at high risk of malaria infection with the most affected being pregnant women, children and the elderly. Fast accurate and scalable malaria-diagnosis methods such as ours will improve health and wealth on large sectors of the population living in extreme poverty across Nigeria and more importantly across the sub-Saharan Africa region (i.e. Togo, Ghana, Cameroon among others).The impact of our research is enormous as rapid and reliable accurate diagnosis of malaria, and therefore its accurate prompt treatment, is a crucial challenge for fulfilling the international development goals for the sub-Saharan African region and other regions of the World affected with malaria.