A new artificial intelligence-powered deep learning model has helped radiologists in China to distinguish COVID-19 from community-acquired pneumonia and other lung diseases in chest CT imaging.
Developed as part of a six-hospital study, scientists refined the model using 4,356 exams from 3,322 patients. The COVID-19 Detection Neural Network—or COVNet for short—scored high marks, notching 90% sensitivity and 96% specificity for diagnosing coronavirus, experts reported Thursday in Radiology.
“These results demonstrate that a machine learning approach using convolutional networks model has the ability to distinguish COVID-19 from community-acquired pneumonia,” concluded Lin Li, from the Department of Radiology at Wuhan Huangpi People's Hospital in China, and colleagues.
Li and colleagues included hundreds of CT scans in the dataset, which displayed community-acquired pneumonia and other lung ailments. Their model also scored high marks in differentiating such diseases from novel coronavirus, with a 87% sensitivity rate and 92% specificity rate.
With radiologists cautioning about overlap between coronavirus imaging findings and other lung issues, Li and colleagues believe AI can provide a useful assist to radiologists concerned about specificity.
“There is overlap in the chest CT imaging findings of all viral pneumonias with other chest diseases that encourages a multidisciplinary approach to the final diagnosis used for patient treatment,” they added.
Marty Stempniak has covered healthcare since 2012, with his byline appearing in the American Hospital Association's member magazine, Modern Healthcare and McKnight's. Prior to that, he wrote about village government and local business for his hometown newspaper in Oak Park, Illinois. He won a Peter Lisagor and Gold EXCEL awards in 2017 for his coverage of the opioid epidemic.