BloodCounts! Consortium with NHSBT scientist wins Trinity Challenge Prize for breakthrough in infectious disease detection

A novel tool that uses artificial intelligence to detect new disease outbreaks sooner, that was first developed by an NHSBT scientist and his colleague at the University of Cambridge, has today been awarded a substantial funding prize by the Trinity Challenge. This marks a significant moment in international efforts to counter the growing threat of future pandemics.

The tool – BloodCounts! – uses data from the routine full blood count test and powerful AI-based techniques to provide a “Tsunami-like” early warning system for new disease outbreaks and is the brainchild of NHSBT’s Dr Nicholas Gleadall and Dr Michael Roberts at University of Cambridge.

Unlike many current test methods their approach doesn't require any prior knowledge of a specific disease pathogen to work. Instead, they use data from common full blood count tests to look for changes in blood associated with infection.

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 tools to detect new emerging infectious disease outbreaks as early as possible. To drive development of these tools, the Trinity Challenge, a global call for solutions to this problem, was created.

Dr Nicholas Gleadall said, “We realised that hundreds of millions of full blood count tests were being performed every day worldwide, and this meant that we could apply our AI-methods at population scale.

“Usually the rich measurement data are discarded after summary results have been reported, but by working with Cambridge University, Barts Health London and University College London NHS Hospitals, we have rescued throughout the pandemic the rich data from 2.8 million full blood count tests.”

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 of large populations - alerting public health agencies to potential outbreaks of infection.

This unique solution is a powerful demonstration of how the application of AI-based methods, built upon rigorous mathematics from the Department of Applied Mathematics and Theoretical Physics at the University of Cambridge, 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. 

Professor Bryan Williams, the Director of the NIHR University College London Hospitals Biomedical Research Centre who was an early supporter of applying AI to the full blood count data, said - “The BloodCounts! approach has huge potential and if this works, it could provide a readily scalable and cheap population surveillance method for outbreak detection of SARS-CoV-2 and other viruses.

“A major advantage is that the NHS already performs more than 100 million FBC tests every year, with over half of these performed by general practitioners in the community, so the programme aims to get more information from tests we already perform.”