Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT

Li, Lin; Qin, Lixin; Xu, Zeguo; Yin, Youbing; Wang, Xin; Kong, Bin; Bai, Junjie; Lu, Yi; Fang, Zhenghan; Song, Qi; Cao, Kunlin; Liu, Daliang; Wang, Guisheng; Xu, Qizhong; Fang, Xisheng; Zhang, Shiqin; Xia, Juan; Xia, Jun

BACKGROUND: Coronavirus disease has widely spread all over the world since the beginning of 2020. It is desirable to develop automatic and accurate detection of COVID-19 using chest CT. PURPOSE: To develop a fully automatic framework to detect COVID-19 using chest CT and evaluate its performances. MATERIALS AND METHODS: In this retrospective and multi-center study, a deep learning model, COVID-19 detection neural network (COVNet), was developed to extract visual features from volumetric chest CT exams for the detection of COVID-19. Community acquired pneumonia (CAP) and other non-pneumonia CT exams were included to test the robustness of the model. The datasets were collected from 6 hospitals between August 2016 and February 2020. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC), sensitivity and specificity. RESULTS: The collected dataset consisted of 4356 chest CT exams from 3,322 patients. The average age is 49±15 years and there were slightly more male patients than female (1838 vs 1484; p-value=0.29). The per-exam sensitivity and specificity for detecting COVID-19 in the independent test set was 114 of 127 (90% [95% CI: 83%, 94%]) and 294 of 307 (96% [95% CI: 93%, 98%]), respectively, with an AUC of 0.96 (p-value<0.001). The per-exam sensitivity and specificity for detecting CAP in the independent test set was 87% (152 of 175) and 92% (239 of 259), respectively, with an AUC of 0.95 (95% CI: 0.93, 0.97). CONCLUSIONS: A deep learning model can accurately detect COVID-19 and differentiate it from community acquired pneumonia and other lung diseases.