Pioneering Artificial Intelligence (AI) technology, developed by experts at the University of the West of Scotland (UWS), is capable of accurately diagnosing Covid-19 in just a few minutes. 
The groundbreaking program can detect the virus far more quickly than a PCR test; which typically takes around 2-hours.
It is hoped that the technology can eventually be used to help relieve strain on hard-pressed Accident and Emergency departments, particularly in countries where PCR tests are not readily available.
The state-of-the-art technique utilizes x-ray technology, comparing scans to a database of around 3000 images, belonging to patients with Covid-19, healthy individuals, and people with viral pneumonia.
It then uses an AI process known as a deep convolutional neural network, an algorithm typically used to analyze visual imagery, to make a diagnosis. During an extensive testing phase, the technique proved to be more than 98% accurate.
Professor Naeem Ramzan, Director of the Affective and Human Computing for SMART Environments Research Centre at UWS, led the three-person team behind the project, which also involved Gabriel Okolo and Dr. Stamos Katsigiannis. 
He said: “There has long been a need for a quick and reliable tool that can detect Covid-19, and this has become even more true with the upswing of the Omicron variant.
“Several countries are unable to carry out large numbers of Covid tests because of limited diagnosis tools, but this technique utilizes easily accessible technology to quickly detect the virus.
“Covid-19 symptoms are not visible in x-rays during the early stages of infection, so it is important to note that the technology cannot fully replace PCR tests.
“However, it can still play an important role in curtailing the viruses spread especially when PCR tests are not readily available.
“It could prove to be crucial, and potentially life-saving, when diagnosing severe cases of the virus, helping determine what treatment may be required.”
Professor Milan Radosavljevic, Vice-Principal of Research, Innovation, and Engagement at UWS, added: “This is potentially game-changing research. It’s another example of the purposeful, impactful work that has gone on at UWS throughout the pandemic, making a genuine difference in the fight against Covid-19.
“I am incredibly proud of the drive and innovation demonstrated by our internationally renowned academics, as they strive to find solutions to urgent global problems.”
The team now plans to expand the study, incorporating a greater database of x-ray images acquired by different models of x-ray machines, to evaluate the suitability of the approach in a clinical setting.
To read the research in full, visit: https://www.mdpi.com/1424-8220/21/17/5702
UK scientists have developed a new machine learning model for the discovery of genetic risk factors for diseases such as Motor Neuron Disease (MND). 
Designed by researchers from the University of Sheffield in Sheffield, South Yorkshire, England, and the Stanford University School of Medicine in the US, the machine learning tool, named RefMap, has already been utilized by the team to discover 690 risk genes for motor neuron disease, many of which are discoveries.
One of the genes highlighted as a new MND gene, called KANK1, has been shown by the team to produce neurotoxicity in human neurons very similar to that observed in the brains of patients. Although at an early stage, this is potentially a new target for the design of new drugs.
Dr. Johnathan Cooper-Knock, from the University of Sheffield’s Neuroscience Institute, said: “This new tool will help us to understand and profile the genetic basis of MND. Using this model we have already seen a dramatic increase in the number of risk genes for MND, from approximately 15 to 690.
“Each new risk gene discovered is a potential target for the development of new treatments for MND and could also pave the way for genetic testing for families to work out their risk of disease.”
The 690 new genes identified by RefMap lead to a five-fold increase in discovered heritability, a measure that describes how much of the disease is due to a variation in genetic factors.
“RefMap identifies risk genes by integrating genetic and epigenetic data. It is a generic tool and we are applying it to more diseases in the lab,” Sai Zhang, Ph.D., instructor of genetics at the Stanford University School of Medicine said.
Michael Snyder, Ph.D., professor and chair of the department of genetics at the Stanford School of Medicine and also the corresponding author of this work added: “By doing machine learning for genome analysis, we are discovering more hidden genes for human complex diseases such as MND, which will eventually power personalized treatment and intervention.”
