Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy
Lin Li, Lixin Qin, Zeguo Xu, Youbing Yin, Xin Wang, Bin Kong, Junjie Bai, Yi Lu, Zhenghan Fang, Qi Song, Kunlin Cao, Daliang Liu, Guisheng Wang, Qizhong Xu, Xisheng Fang, Shiqin Zhang, Juan Xia, Jun Xia, Lin Li, Lixin Qin, Zeguo Xu, Youbing Yin, Xin Wang, Bin Kong, Junjie Bai, Yi Lu, Zhenghan Fang, Qi Song, Kunlin Cao, Daliang Liu, Guisheng Wang, Qizhong Xu, Xisheng Fang, Shiqin Zhang, Juan Xia, Jun Xia
Abstract
Background Coronavirus disease 2019 (COVID-19) 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 performance. Materials and Methods In this retrospective and multicenter study, a deep learning model, the COVID-19 detection neural network (COVNet), was developed to extract visual features from volumetric chest CT scans for the detection of COVID-19. CT scans of community-acquired pneumonia (CAP) and other non-pneumonia abnormalities were included to test the robustness of the model. The datasets were collected from six hospitals between August 2016 and February 2020. Diagnostic performance was assessed with the area under the receiver operating characteristic curve, sensitivity, and specificity. Results The collected dataset consisted of 4352 chest CT scans from 3322 patients. The average patient age (±standard deviation) was 49 years ± 15, and there were slightly more men than women (1838 vs 1484, respectively; P = .29). The per-scan sensitivity and specificity for detecting COVID-19 in the independent test set was 90% (95% confidence interval [CI]: 83%, 94%; 114 of 127 scans) and 96% (95% CI: 93%, 98%; 294 of 307 scans), respectively, with an area under the receiver operating characteristic curve of 0.96 (P < .001). The per-scan sensitivity and specificity for detecting CAP in the independent test set was 87% (152 of 175 scans) and 92% (239 of 259 scans), respectively, with an area under the receiver operating characteristic curve of 0.95 (95% CI: 0.93, 0.97). Conclusion A deep learning model can accurately detect coronavirus 2019 and differentiate it from community-acquired pneumonia and other lung conditions. © RSNA, 2020 Online supplemental material is available for this article.
Figures
References
- Chen N, Zhou M, Dong X, et al. . Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. The Lancet 2020 Jan 30. doi: 10.1016/S0140-6736(20)30211-7.
- Wang D, Hu B, Hu C, et al. . Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China. Jama 2020.
- Li Q, Guan X, Wu P, et al. . Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia. The New England Journal of Medicine 2020.
- Holshue ML, DeBolt C, Lindquist S, et al. . First Case of 2019 Novel Coronavirus in the United States. New England Journal of Medicine 2020 Jan 31. doi:10.1056/NEJMoa2001191.
- Ai T, Yang Z, Hou H, et al. . Correlation of Chest CT and RT-PCR Testing in Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases. Radiology 2020. 10.1148/radiol.2020200642.
- Fang Y, Zhang H, Xie J, et al. . Sensitivity of Chest CT for COVID-19: Comparison to RT-PCR. Radiology. 2020. 10.1148/radiol.2020200432
- Chung M, Bernheim A, Mei X, et al. . CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV). Radiology 2020: 200230.
- Huang C, Wang Y, Li X, et al. . Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The Lancet 2020 Jan 24. doi: 10.1016/S0140-6736(20)30183-5.
- Xia C, Li X, Wang X, et al. . A Multi-modality Network for Cardiomyopathy Death Risk Prediction with CMR Images and Clinical Information. International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2019.
- Kong B, Wang X, Bai J, et al. . Learning tree-structured representation for 3D coronary artery segmentation. Computerized Medical Imaging and Graphics 2020, 80: 101688.
- Ye H, Gao F, Yin Y, et al. . Precise diagnosis of intracranial hemorrhage and subtypes using a three-dimensional joint convolutional and recurrent neural network. European Radiology, 2019, 29: 6191–6201.
- Kermany DS, Goldbaum M, Cai W, et al. . Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning. Cell 2018, 172, 1122–1131.
- Rajaraman S, Candemir S, Kim I, et al. . Visualization and interpretation of convolutional neural network predictions in detecting pneumonia in pediatric chest radiographs. Applied Sciences 8.10 (2018): 1715.
- Depeursinge A, Chin AS, Leung AN, et al. . Automated classification of usual interstitial pneumonia using regional volumetric texture analysis in high-resolution CT. Investigative Radiology 50.4 (2015): 261.
- Anthimopoulos M, Christodoulidis S, Ebner L, et al. . Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Transactions on Medical Imaging 35.5 (2016): 1207-1216.
- He K, Zhang X, Ren S, et al. . Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016.
- Ronneberger O, Philipp F, Thomas B. U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2015.
- DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988 (44), 837–845.
- Selvaraju RR, Cogswell M, Das A, et al. . Grad-cam: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision. 2017.
Source: PubMed