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A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19)
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Abstract
Objective: The outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) has caused more than 26 million cases of Corona virus disease (COVID-19) in the world so far. To control the spread of the disease, screening large numbers of suspected cases for appropriate quarantine and treatment are a priority. Pathogenic laboratory testing is typically the gold standard, but it bears the burden of significant false negativity, adding to the urgent need of alternative diagnostic methods to combat the disease. Based on COVID-19 radiographic changes in CT images, this study hypothesized that artificial intelligence methods might be able to extract specific graphical features of COVID-19 and provide a clinical diagnosis ahead of the pathogenic test, thus saving critical time for disease control. Methods: We collected 1065 CT images of pathogen-confirmed COVID-19 cases along with those previously diagnosed with typical viral pneumonia. We modified the inception transfer-learning model to establish the algorithm, followed by internal and external validation. Results: The internal validation achieved a total accuracy of 89.5% with a specificity of 0.88 and sensitivity of 0.87. The external testing dataset showed a total accuracy of 79.3% with a specificity of 0.83 and sensitivity of 0.67. In addition, in 54 COVID-19 images, the first two nucleic acid test results were negative, and 46 were predicted as COVID-19 positive by the algorithm, with an accuracy of 85.2%. Conclusion: These results demonstrate the proof-of-principle for using artificial intelligence to extract radiological features for timely and accurate COVID-19 diagnosis. Key Points: • The study evaluated the diagnostic performance of a deep learning algorithm using CT images to screen for COVID-19 during the influenza season. • As a screening method, our model achieved a relatively high sensitivity on internal and external CT image datasets. • The model was used to distinguish between COVID-19 and other typical viral pneumonia, both of which have quite similar radiologic characteristics.
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China
Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30183-5 · Cited by 48,884
Rethinking the Inception Architecture for Computer Vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138
Zhou, F., Yu, T., Du, R., Fan, G., Liu, Y., Liu, Z., Xiang, J., Wang, Y., Song, B., Gu, X., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30566-3 · Cited by 27,263
Chen, N., Zhou, M., Dong, X., Qu, J., Gong, F., Han, Y., Qiu, Y., Wang, J., Liu, Y., Wei, Y., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30211-7 · Cited by 21,415
Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia
Li, Q., Guan, X., Wu, P., Wang, X., Zhou, L., Tong, Y., Ren, R., Leung, K. S. M., Lau, E. H. Y., Wong, J. Y., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2001316 · Cited by 16,575
Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation
Powers, D. M. W. (2020) · arXiv · doi:10.48550/arxiv.2010.16061 · Cited by 5,440
Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning
Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C. S., Liang, H., Baxter, S. L., McKeown, A., Yang, G., Wu, X., Yan, F., et al. (2018) · Cell · doi:10.1016/j.cell.2018.02.010 · Cited by 4,842
CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV)
Chung, M., Bernheim, A., Mei, X., Zhang, N., Huang, M., Zeng, X., Cui, J., Xu, W., Yang, Y., Fayad, Z. A., et al. (2020) · Radiology · doi:10.1148/radiol.2020200230 · Cited by 2,751
Self-citationMaghded, H. S., Ghafoor, K. Z., Sadiq, A. S., Curran, K., Rawat, D. B., & Rabie, K. (2020) · 2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI) · doi:10.1109/iri49571.2020.00033 · Cited by 279
Research works printed but not linked: 17
- Velavan TP, Meyer CG (2020) The COVID-19 epidemic. Trop Med Int Health 25:278–280 · DOI · Paper page
- Wang D, Hu B, Hu C et al (2020) Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. JAMA. https://doi.org/10.1001/jama.2020.1585 · DOI · Paper page
- Chu DKW, Pan Y, Cheng SMS et al (2020) Molecular diagnosis of a novel coronavirus (2019-nCoV) causing an outbreak of pneumonia. Clin Chem 66:549–555 · DOI · Paper page
- Corman VM, Landt O, Kaiser M et al (2020) Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR. Euro Surveill 25 · DOI · Paper page
- Zhang N, Wang L, Deng X et al (2020) Recent advances in the detection of respiratory virus infection in humans. J Med Virol 92:408–417 · DOI · Paper page
- Gomez P, Semmler M, Schutzenberger A, Bohr C, Dollinger M (2019) Low-light image enhancement of high-speed endoscopic videos using a convolutional neural network. Med Biol Eng Comput 57:1451–1463 · DOI
- Choe J, Lee SM, Do KH et al (2019) Deep learning-based image conversion of CT reconstruction kernels improves radiomics reproducibility for pulmonary nodules or masses. Radiology 292:365–373 · DOI · Paper page
- Negassi M, Suarez-Ibarrola R, Hein S, Miernik A, Reiterer A (2020) Application of artificial neural networks for automated analysis of cystoscopic images: a review of the current status and future prospects. World J Urol. https://doi.org/10.1007/s00345-019-03059-0 · DOI
- Wang P, Xiao X, Glissen Brown JR et al (2018) Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy. Nat Biomed Eng 2:741–748 · DOI · Paper page
- Martínez-Camblor P, Pardo Fernández JC (2019) The Youden index in the generalized receiver operating characteristic curve context. Int J Biostat 15(1):2018–0060 · DOI
- Carletta J (1996) Assessing agreement on classification tasks: the kappa statistic. Comput Linguist 22:249–254
- Ai T, Yang Z, Hou H et al (2020) Correlation of chest CT and RT-PCR testing for coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology 296:E32–E40 · DOI · Paper page
- Ching T, Himmelstein DS, Beaulieu-Jones BK et al (2018) Opportunities and obstacles for deep learning in biology and medicine. J R Soc Interface 15 · DOI · Paper page
- Yang X, He X, Zhao J, Zhang Y, Zhang S, Xie P (2003) COVID-CT-Dataset: a CT image dataset about COVID-19. ArXiv 2020:13865
- Farid AA, Selim GI, Khater H (2020) A novel approach of CT images feature analysis and prediction to screen for corona virus disease. Int J Sci Eng Res 11(3):1141. https://doi.org/10.14299/ijser.2020.03.02 · DOI
- Mor NS (2020) Corona Virus Disease (COVID-19) Screening with deep learning using CT images. https://www.researchgate.net/profile/Nuriel_Mor/publication. Accessed April 2020.
- Grapov D, Fahrmann J, Wanichthanarak K, Khoomrung S (2018) Rise of deep learning for genomic, proteomic, and metabolomic data integration in precision medicine. OMICS 22:630–636 · DOI
Cited by
Wynants, L., Van Calster, B., Bonten, M. M., Collins, G. S., Debray, T. P., De Vos, M., Haller, M. C., Heinze, G., Moons, K. G., Riley, R. D., et al. (2020) · doi:10.1101/2020.03.24.20041020 · Cited by 3,070
Can AI help in screening viral and COVID-19 pneumonia?
Chowdhury, M. E. H., Rahman, T., Khandakar, A., Mazhar, R., Kadir, M. A., Mahbub, Z. B., Islam, K. R., Khan, M. S., Iqbal, A., Emadi, N. A., et al. (2020) · IEEE Access · doi:10.1109/access.2020.3010287 · Cited by 1,913
Shi, F., Wang, J., Shi, J., Wu, Z., Wang, Q., Tang, Z., He, K., Shi, Y., & Shen, D. (2021) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2020.2987975 · Cited by 1,404
Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images
Fan, D.-P., Zhou, T., Ji, G.-P., Zhou, Y., Chen, G., Fu, H., Shen, J., & Shao, L. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2996645 · Cited by 1,259
Digital technologies in the public-health response to COVID-19
Budd, J., Miller, B. S., Manning, E. M., Lampos, V., Zhuang, M., Edelstein, M., Rees, G., Emery, V. C., Stevens, M. M., Keegan, N., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-1011-4 · Cited by 1,195
Metadata
- Authors
- Wang, S., Kang, B., Ma, J., Zeng, X., Xiao, M., Guo, J., Cai, M., Yang, J., Li, Y., Meng, X., & Xu, B.
- Venue
- European Radiology
- Published
- 2021-02-24
- Volume
- 31
- Issue
- 8
- Pages
- 6096-6104
- ISSN
- 0938-7994 (ISSN-L)
- 1432-1084
- OpenAlex
- W3133191822
- Cited by
- 1,677
- References
- 10
- FWCI
- 156.94
- Type
- journal article
- Language
- en
- Open access
- hybrid
- Licence
- creativecommons.org/licenses/by/4.0 (tdm)
- creativecommons.org/licenses/by/4.0 (vor)
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- Wang, S., Kang, B., Ma, J., Zeng, X., Xiao, M., Guo, J., Cai, M., Yang, J., Li, Y., Meng, X., & Xu, B. (2021). A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19). European Radiology. https://doi.org/10.1007/s00330-021-07715-1
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