Doctors in a Leicester hospital have installed a £1m disease detection facility inspired by Star Trek to help diagnose patients more quickly.
The equipment, developed by researchers from Leicester University, uses an array of advanced technology that draws from space and pollution research to provide information about a patient’s condition without invasive and time-consuming tests.
A team at Leicester Royal Infirmary will next month begin testing the equipment to see how well it could help rapidly diagnose a range of conditions, from sepsis, to cancer, to drug overdoses.
The project is a collaboration between members of the university’s chemistry and emergency medicine departments and space research centre, who wanted to create something that performed a similar function to the Star Trek ‘tricorder’ scanner.
The aim is to be able to build up a picture of what is wrong with a patient much sooner in the diagnosis period and with more information, said Leicester’s professor of emergency medicine Tim Coats.
‘We’re trying to replace some of the things that are unpleasant and invasive at the moment, that involve sticking needles into people, with measurements that don’t need those unpleasant, painful procedures,’ he told The Engineer.
The technology combines a visual analysis of the patient’s appearance with a breakdown of chemicals found in their breath and data on their cardiovascular activity, with all data collected within 15 minutes.
It makes use of Leicester’s hyperspectral imaging technology, which analyses a range of electromagnetic radiation and was originally developed to look for life on other planets, to assess the patient’s temperature and blood and oxygen distribution.
A specially designed mass spectrometer — originally used to detect air pollution — analyses chemical molecules in the patient’s breath in real time by performing one-million mass-spectrum measurements a second.
Paul Monks, Leicester’s professor of physical chemistry, said the long-term aim was for doctors to be able to read all the information on a handheld device and the challenge was to integrate all of the data.
‘If you want to look at the holistic nature of a patient non-invasively you’ve got to add the different data streams together,’ he said. ‘So what we’re using is multi-variance statistical techniques, to merge these things together.’
The research is funded by a £500,000 grant from the Higher Education Funding Council for England (HEFCE). From 1 October, the equipment will be tested to see which conditions it could be best used to diagnose or rule out.