A team of infectious disease and clinical machine learning experts including Professor David Clifton has developed an Artificial Intelligence test that identifies COVID-19 within one hour in patients arriving in emergency departments.
Currently, testing for COVID-19 is by a molecular analysis of a nose and throat swab, called a Polymerase Chain Reaction (PCR). However, this typically has a turnaround time of 12-48 hours and requires specialist equipment and staff. The new ‘CURIAL’ AI test assesses data routinely collected during the first hour in emergency departments, such as blood tests and vital signs, to determine the chance of a patient testing positive for Coronavirus.
The study has been running since March and began by developing machine learning algorithms trained on data from confirmed cases and pre-pandemic controls to detect subtle differences. It was hoped these algorithms would allow the level of risk of having the illness to be determined. Once trained, the algorithms had to be assessed for their accuracy, and the two early-detection models were put to the test in a real hospital setting. The results have now been published in a preprint article to provide an early (as yet not peer reviewed) report of the work.
David and the team of researchers led by Dr Andrew Soltan, an NIHR Academic Clinical Fellow at the John Radcliffe Hospital, are now working hard to rapidly trial the CURIAL AI as a clinically useful tool for the NHS.
David explained, "With many of our clinical colleagues working on the front lines to fight COVID-19, data scientists in Healthcare AI have a supporting role to play by constructing tools to help care for patients. The unique ecosystem at Oxford between hospitals and clinical AI teams gives us a great opportunity to contribute to the international effort against Coronavirus. This project...is a great example of what can be done, and at very great pace, to fast-track the development of technologies to help in the current pandemic – and to increase the resilience of the country’s healthcare system for any future events."
It is hoped the development of these techniques will also inform clinical teams in the early stages of future pandemics, and expedite implementation of appropriate public health measures.
This news story is an edited version of the original article on the Department of Engineering Science website.