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WEDNESDAY, March 25, 2020 (HealthDay News) — Artificial intelligence allows coronavirus disease 2019 (COVID-19) to be detected and differentiated from community-acquired pneumonia (CAP) on chest computed tomography (CT), according to a study published online March 19 in Radiology<\/i>.<\/p>\n
Lin Li, from Wuhan Huangpi People’s Hospital in China, and colleagues developed a fully automatic framework to detect COVID-19 using chest CT in a retrospective and multicenter study. To extract visual features from volumetric chest CT exams, a deep learning model COVID-19 detection neural network was developed. To test the robustness of the model, CAP and other nonpneumonia CT exams were included.<\/p>\n
The dataset included 4,356 chest CT exams from 3,322 patients obtained from six hospitals between August 2016 and February 2020. The researchers found that in the independent test set, per-exam sensitivity and specificity for detecting COVID-19 were 90 and 96 percent, respectively, with an area under the receiver operating characteristic curve (AUC) of 0.96. For detecting CAP in the independent testing set, the per-exam sensitivity and specificity were 87 and 92 percent, respectively, with an AUC of 0.95.<\/p>\n
“This study focuses on whether one exam is COVID-19 or not, but has not addressed categorizing the disease into different severities,” the authors write. “As a next step, it would be important to not only predict the presence of COVID-19, but also the severity degree to further help monitor and treat patients.”<\/p>\n