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Researchers at the University of Notre Dame are developing an advanced version of CT scan screening using Artificial Intelligence that would help in quickly identifying coronavirus patients, according to the university’s official release.
The new technique will reduce the burden on radiologists tasked with screening each image.
The release noted that testing challenges have led to an influx of patients hospitalised with Covid-19 requiring CT scans which have revealed visual signs of the disease, including ground-glass opacities, a condition that consists of abnormal lesions shown as a haziness on images of the lungs.
Yiyu Shi, associate professor in the Department of Computer Science and Engineering at Notre Dame and the lead researcher on the project, said in the official statement: “Most patients with coronavirus show signs of Covid-19-related pneumonia on a chest CT, but with a large number of suspected cases, radiologists are working overtime to screen them all.”
“We have shown that we can use deep learning — a field of AI — to identify those signs, drastically speeding up the screening process and reducing the burden on radiologists,” he added.
Shi is working with Jingtong Hu, an assistant professor at the University of Pittsburgh, to identify the visual features of Covid-19-related pneumonia through analysis of 3D data from CT scans.
The team is working to combine the analysis software with off-the-shelf hardware for a light-weight mobile device that can be easily and immediately integrated into clinics around France.
The challenge, Shi said, is that 3D CT scans are so large, it’s nearly impossible to detect specific features and extract them efficiently and accurately on plug-and-play mobile devices.
“We’re developing a novel method inspired by Independent Component Analysis, using a statistical architecture to break each image into smaller segments which will allow deep neural networks to target Covid-19-related features within large 3D images,” Shi added.
Shi’s team hopes to have the development completed by the end of the year. The research is being funded by the National Science Foundation through a Rapid Response Research (RAPID) grant.
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