Scientists have developed a world-first diagnostic test, powered by artificial intelligence, that can identify known respiratory viruses within five minutes from just one nasal or throat swab.
The new diagnostic test could replace current methods limited to testing for only one infection, such as a lateral flow test for COVID-19, or otherwise are either lab-based and time-consuming or fast and less accurate.
The new virus detection and identification methodology are described in a paper published in ACS Nano.
The paper demonstrates how machine learning can significantly improve the efficiency, accuracy, and time taken to not only identify different types of viruses but also differentiate between strains.
Nicolas Shiaelis and Dr Nicole Robb collaborated with the John Radcliffe Hospital to validate the new method.
The ground-breaking testing technology combines molecular labeling, computer vision, and machine learning to create a universal diagnostic imaging platform that looks directly at a patient sample and can identify which pathogen is present in a matter of seconds – much like facial recognition software, but for germs.
Preliminary research demonstrated that the test could identify the COVID-19 virus in patient samples and further work determined that the test could be used to diagnose multiple respiratory infections, within five minutes and with over 97% accuracy.
Dr Robb and Nicolas Shiaelis founded Pictura Bio, a University of Oxford spinout, that now licenses the technology. They are currently looking for further investment to accelerate development and get it to the front line of healthcare.
Dr Robb said: ‘Cases of respiratory infections this winter have hit record-breaking highs, increasing the number of people seeking medical help. This combined with the COVID-19 backlog, staff shortages, tighter budgets and an aging population puts the NHS and its workforce under immense and unsustainable pressure.
‘Our simplified diagnostic testing method is quicker and more cost-effective, accurate and future-proof than any other tests currently available.
If we want to detect a new virus, all we need to do is retrain the software to recognize it, rather than develop a whole new test. Our findings demonstrate the potential for this method to revolutionize viral diagnostics and our ability to control the spread of respiratory illnesses.’
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