FASt-Mal AI Assisted MalariaDiagnosticDetectionIdentificationClassificationTool
Quicker accurate malaria diagnosis will enable faster delivery of clinical services to facilitate International Development Goals for the sub-Saharan African region and other regions of the world affected by malaria.
The FASt-mal Diagnosis System is led by an international multidisciplinary team from UCL Computer Science, in equal collaboration with the College of Medicine of the University of Ibadan (COMUI), Nigeria. The team, comprised Delmiro Fernandez-Reyes, Reader in Digital Health & Intelligent Systems at UCL Computer Science, Mandayam A. Srinivasan and John Shawe-Taylor (UCL Department of Computer Science) and Biobele J. Brown, Ikeoluwa Lagunju and Olugbemiro Sodeinde (COMUI Department of Paediatrics), carries out research to produce a novel fast robotic-automated computational system capable of reliably diagnosing malaria in sub-Saharan West-Africa.
In 2017, the team was awarded a £1.5 million EPSRC Global Challenges Research Fund (GCRF). The funding is being used to carry out engineering (robotics), computational research (computer vision and machine learning) and digital health clinical research (paediatric infectious diseases) to design, implement, deploy and test a fully automated system capable of tackling the challenges posed by human-operated light-microscopy currently used in the diagnosis of malaria.
Access to effective malaria diagnosis is a challenge faced by all developing countries where malaria is endemic. Human-microscopic examination of blood smears remains the ‘gold standard’ for malaria diagnosis and despite its major drawbacks, other non-microscopic methodologies have not been able to outperform it. Presumptive treatment for malaria –i.e. without microscopic confirmation of the disease- is wasteful of drugs; ineffective if the diagnosis was wrong; a drain on limited healthcare resources and fuels antimalarial resistance. This scenario has prompted Global Health organisations to emphasise the urgent need for tools to overcome the deficiencies of human-operated optical-microscopy malaria diagnosis and other non-microscopic tests.
The research and development of the FASt-Mal project is in collaboration with the UCL TouchLab, a facility pioneering research in Robotic and CS technologies for the Life Sciences and Biology. Find out more about the TouchLab on their website.
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Research
The funded research aims to overcome these diagnostic challenges by replacing human-expert optical-microscopy with a robotic automated computer-expert system that assesses similar digital-optical-microscopy representations of the disease. The Fast, Accurate and Scalable Malaria (FASt-Mal) diagnosis system harnesses the power of state-of-the-art machine learning approaches to support clinical decision making.
UCL Computer Science is bringing together UCL’s wealth of cross discipline intellectual capital, to find innovative, workable solutions to Global Health problems. The University of Ibadan is the oldest and one the strongest academic environment in the region providing training to professionals for the whole sub-Saharan region (West, East and South) and internationally. The GCRF funding will strengthen this regional centre of excellence and with UCL’s input will facilitate knowledge transfer to other countries affected with malaria.
Biobele J. Brown comments:
In Nigeria alone, malaria is one of the most common causes of death in children below five-years of age. Rapid, reliable and accurate diagnosis of the disease, giving way to prompt, adequate treatment, is a crucial challenge for fulfilling the Sustainable Development Goals and impacting on the sub-Saharan African region and other regions of the World affected by malaria.
Delmiro Fernandez-Reyes comments:
In Nigeria alone, malaria is one of the most common causes of death in children below five-years of age. Rapid, reliable and accurate diagnosis of the disease, giving way to prompt, adequate treatment, is a crucial challenge for fulfilling the Sustainable Development Goals and impacting on the sub-Saharan African region and other regions of the World affected by malaria.
The FASt-Mal system will have an impact on already-stretched clinical services in the region by enabling healthcare providers to divert human and financial resources to further improve healthcare provision to those more affected by malaria, pregnant women and children. The work provides a clear example of how engineering and digital-technologies research at unison with clinical research can underpin successful and sustainable healthcare provision in the sub-Saharan region and therefore significantly improve Global Health – with appropriate timing on World Malaria Day on 25 April 2017.
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. Although Nigeria is a low-to-middle income emerging economy, an estimated 50% to 70% of its population lives in extreme poverty. Large all-year-round lethal malaria morbidity and mortality burden has severely hindered wellbeing and the economic development of the West sub-Saharan geopolitical region.
About Malaria
Malaria is a mosquito-borne infectious disease affecting humans and other animals. It is caused by parasitic protozoans belonging to the Plasmodium species. Human malaria is caused by five Plasmodium species (P. falciparum or -lethal malaria-; P. vivax; P. ovale; P. malariae and P. knowlesi).
Malaria is widespread in the tropical and subtropical regions including much of Sub-Saharan Africa, Asia, and Latin America. In 2015, there were an estimated 296 million cases of malaria worldwide resulting in an estimated under a million deaths. Although current malaria control strategies have resulted in speculative estimates of a decrease on mortality rates, success on reducing the burden of disease Worldwide has remained largely elusive.
