Work by Waterloo Engineering researchers launched early in the COVID-19 pandemic tops a list of the most cited papers in a leading academic journal.
Results recently released by Google Scholar Metrics show a paper on the use of chest X-rays and artificial intelligence (AI) analysis to screen for COVID-19 has been cited over 3,700 times, more than any other paper published in Nature Scientific Reports from 2020 to 2024.
The paper, COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images, was first made available in early 2020 as open-source work by Dr. Alexander Wong, a professor of systems design engineering, and his research team at the Vision and Image Processing Research Group.
After an appeal to the scientific community for help to develop a reliable screening tool to augment COVID-19 swab tests then in short supply, the research team – which also included Linda Wang and Zhong Qiu Lin – received hundreds of inquiries and offers from researchers, scientists and AI experts around the world.