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Researchers create ‘COVID computer’ to speed

Article Highlights | 1-Jul-2022

Researchers at the University of Leicester have developed a new AI tool that can detect COVID-19.

University of Leicester

Researchers at the University of Leicester have developed a new AI tool that can detect COVID-19.

The software analyzes the chest CT scan and uses in-depth learning algorithms to accurately diagnose the disease. With an accuracy rate of 97.86%, it is now the most successful Covid-19 diagnostic tool in the world.

Currently, the diagnosis of COVID-19 is based on nucleic acid testing, or PCR tests as it is commonly known. These tests can generate false negatives and the results can also be affected by hysteresis – when the physical effects of a disease are late in their cause. AI, therefore, offers an opportunity for rapid screening and effective monitoring of COVID-19 cases on a large scale, reducing the burden on physicians.

Professor Yudong Zhang, Professor of Knowledge Discovery and Machine Learning at Leicester University said:

“Our research focuses on the automatic diagnosis of COVID-19 based on a random graph neural network. The results show that our method can locate suspicious regions in chest images automatically and make accurate predictions based on representations.The accuracy of the system means that it can be used in the clinical diagnosis of COVID-19, which can help control the spread of the virus.We hope that, in the future, this type of technology will allow for automated computer-assisted diagnosis. will require manual intervention, to create a smarter, more efficient health care service.

Researchers are now further developing this technology in the hope that the Covid computer can replace the need for radiologists to diagnose COVID-19 in clinics. The software, which can even be deployed on portable devices such as smart phones, will also be adapted and expanded to detect and diagnose other diseases (such as breast cancer, Alzheimer’s Disease, and cardiovascular diseases). .

‘NAGNN: Classification of COVID-19 based on neighbor-known representation from deep graph neural network’ published in International Journal of Intelligent Systems.

Disclaimer: AAAS and EurekAlert! is not responsible for the accuracy of news releases posted by EurekAlert! by contributing to institutions or for the use of any information through the EurekAlert system.

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