Robot-automated system can accurately diagnose malaria

Malaria kills 1 million people each year and 75% of these deaths occur in Africa.

Fast and accurate diagnosis is essential for effective treatment, but access is a challenge faced by developing countries where malaria is endemic.

Human-microscopic examination of blood smears is the current ‘gold standard’ for diagnosis. However, a major setback of this method is in endemic countries, many partially-immune individuals may be parasitized but not ill, or ill from another disease. 

Dr Delmiro Fernandez-Reyes is carrying out research to produce a fast robotic-automated computational system capable of reliably diagnosing malaria in sub-Saharan West Africa. The FASt-Mal system uses machine learning to support clinical decision making. 

In 2017, the team were awarded by the £1.5 million EPSRC Global Research Fund. The project is in collaboration with the UCL TouchLab. 

Related links:
UCL FASt-Mal Diagnostic System