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.