Associate Professor Xin Gao and his group have developed an artificial-intelligence (AI) based solution to help increase COVID-19 testing accuracy. Identifying cases of early stage infection has been particularly challenging for frontline clinicians. Gao's AI-based model, which aims to increase accuracy, has been put to immediate use at King Faisal Specialist Hospital (KFSH) in Riyadh.
"The model was fast to use and each case took approximately less than a minute to be processed. It is expected that such a model will make an important contribution to chest imaging, especially with the current pandemic," said Dr. Riham Eiada of the King Faisal Specialist Hospital.
Supporting clinicians with greater accuracy
To date, the gold standard for confirmation of COVID-19 has been nucleic acid detection. Unfortunately, this method of testing alone has had a high, false-negative rate, especially for patients in the early stages of the disease.
In response, Gao and his group in the Computational Bioscience Research Center (CBRC) proposed a fully-automatic, rapid, accurate, and machine-agnostic method that can segment and quantify the infection regions on CT scans from different sources.
Despite urgent advances in the development of AI-based computer-aided systems for CT-based COVID-19 diagnosis, whereas the existing segmentation methods require a high level of human intervention. The KAUST team has resolved these shortcomings with three novel innovations.
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