A new, 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,” reported Phys.org website. (https://phys.org/news/2023-02-scientists-respiratory-viruses-minutes.html )
The new technology and its tests was presented in a paper published by ACS Nano, and authored by Department of Physics DPhil student Nicolas Shiaelis along with corresponding authors Professor Achillefs Kapanidis from the Department of Physics and Dr. Nicole Robb from the University of Warwick and Visiting Lecturer at Oxford’s Department of Physics.
“In the study, the researchers began by labeling viruses with single-stranded DNA in over 200 clinical samples from John Radcliffe Hospital. Images of labeled samples were captured using a commercial fluorescence microscope and processed by custom machine-learning software that is trained to recognize specific viruses by analyzing their fluorescence labels, which show up differently for every virus because their surface size, shape and chemistry vary.
“The results show the technology is able to rapidly identify different types and strains of respiratory viruses, including flu and COVID-19, within five minutes and with >97% accuracy.”
Through machine learning, the technology will be able to “significantly improve the efficiency, accuracy and time taken to not only identify different types of viruses, but also differentiate between strains.”
Dr. Robb explained, “Cases of respiratory infections in winter 2022/23 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 method of diagnostic testing 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.” (https://pubs.acs.org/doi/10.1021/acsnano.2c10159 )
