Machine learning is already contributing to improved diagnoses and treatment of diseases. 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 with malaria.
The funding will be used to carry-out engineering (robotics), computational research (computer vision and machine learning) and digital health clinical research (pediatric 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. 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. Driven by AI in each step, this allows for constant improvement and scalability. Improved smartphone processing also has potential to enable diagnostics “on-the-go” or in remote areas. As smartphone penetration increases, access to mobile diagnostics will expand, magnifying the effect of the improvements in smartphone processing enabling these innovations.