BloodCounts! Consortium wins Trinity Challenge Prize for breakthrough in infectious disease detection
An international consortium of scientists led by College Fellow Professor Carola-Bibiane Schönlieb, Professor of Applied Mathematics at the Department of Applied Mathematics and Theoretical Physics (DAMTP), has been awarded a £1 million prize by the Trinity Challenge to further develop their innovative infectious disease outbreak detection system.
The loss of 3.8 million lives in the ongoing COVID-19 pandemic has highlighted that there is a critical need for simple, affordable, and scalable technologies for early detection of novel emerging infectious disease outbreaks. To drive development of these tools the Trinity Challenge, a global call for solutions to this problem, was set.
The BloodCounts! solution, developed by Dr Michael Roberts and Dr Nicholas Gleadall, uses data from routine blood tests and powerful AI-based techniques to provide a 'Tsunami-like' early warning system for novel disease outbreaks.
Dr Roberts said: “Since the beginning of the pandemic I have been developing AI-based methods to aid in medical decision making for COVID-19 patients, starting with analysis of chest X-ray data. Echoing the observations made by the clinical teams, we saw profound and unique differences in the medical measurements of infected individuals, particularly in their full blood count data. It is these changes that we can train models to detect at scale.”
Unlike many current test methods their approach doesn't require any prior knowledge of a specific pathogen to work, instead, they use full blood count data to exploit the pathogen detecting abilities of the human immune system by observing changes in the blood measurements associated with infection.
As the full blood count is the world’s most common medical laboratory test, with over 3.6 billion being performed worldwide each year, the BloodCounts! team can rapidly apply their methods to scan for abnormal changes in the blood cells of large populations - alerting public health agencies to potential outbreaks of pathogen infection.
This unique solution is a powerful demonstration of how the application of AI-based methods, built upon rigorous mathematics, can lead to huge healthcare benefits when applied in many areas of medicine. It also highlights the importance of strong collaboration between leading organisations, as the development of these algorithms was only possible due the EpiCov data sharing initiative pioneered by Cambridge University Hospitals.
This piece is based on an article first published on the University of Cambridge's Department of Haematology website.
