Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy
Open accessAbstract
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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Our method achieved better performance on both AUC and accuracy compared to VB-Net and COVNet.
“More importantly, our method achieved better performance on both AUC and accuracy compared to VBNet [179] and COVNet [178].”
From Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: a mini-review, two showcases and beyond (2022) · cites this paper as [178]
COVID-Net taking the most discriminative slice as the representation of the whole CT volume ignored the encoding of adjacent slices, leading to bias towards positive cases and low specificity.
“COVID-Net [178] took the most discriminative slice as the representation of the whole CT volume, which ignored the encoding of adjacent slices. This would enforce the model only detect the most discriminative slice, leading to the bias towards positive cases, which could impede the detecting of negative cases that resulted in a low specificity.”
From Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: a mini-review, two showcases and beyond (2022) · cites this paper as [178]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404 · see citing works
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 · see citing works
Self-citationDeLong, E. R., DeLong, D. M., & Clarke-Pearson, D. L. (1988) · Biometrics · doi:10.2307/2531595 · Cited by 22,649 · see citing works
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 · see citing works
Self-citationWang, D., Hu, B., Hu, C., Zhu, F., Liu, X., Zhang, J., Wang, B., Xiang, H., Cheng, Z., Xiong, Y., et al. (2020) · JAMA · doi:10.1001/jama.2020.1585 · Cited by 20,148 · see citing works
Self-citationLa certeza de lo impredecible: Cultura Educación y Sociedad en tiempos de COVID19
Martínez González, M. (2020) · DSPACE System - Metalibrary (University of the Coast) · Cited by 18,463 · see citing works
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 · see citing works
Self-citationProceedings of IEEE International Conference on Computer Vision
(1995) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.1995.466933 · Cited by 13,117 · see citing works
First case of 2019 novel coronavirus in the United States
Holshue, M. L., DeBolt, C., Lindquist, S., Lofy, K. H., Wiesman, J., Bruce, H., Spitters, C., Ericson, K., Wilkerson, S., Tural, A., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2001191 · Cited by 6,030 · see citing works
Ai, T., Yang, Z., Hou, H., Zhan, C., Chen, C., Lv, W., Tao, Q., Sun, Z., & Xia, L. (2020) · Radiology · doi:10.1148/radiol.2020200642 · Cited by 5,697 · see citing works
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 · see citing works
Self-citationSensitivity of Chest CT for COVID-19: Comparison to RT-PCR
Fang, Y., Zhang, H., Xie, J., Lin, M., Ying, L., Pang, P., & Ji, W. (2020) · Radiology · doi:10.1148/radiol.2020200432 · Cited by 3,340 · see citing works
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 · see citing works
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
(1994) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.1994.323798 · Cited by 1,490 · see citing works
Lung pattern classification for interstitial lung diseases using a deep convolutional neural network
Anthimopoulos, M., Christodoulidis, S., Ebner, L., Christe, A., & Mougiakakou, S. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2535865 · Cited by 1,343 · see citing works
Rajaraman, S., Candemir, S., Kim, I., Thoma, G. R., & Antani, S. (2018) · Applied Sciences · doi:10.3390/app8101715 · Cited by 319 · see citing works
Ye, H., Gao, F., Yin, Y., Guo, D., Zhao, P., Lü, Y., Wang, X., Bai, J., Cao, K., Song, Q., et al. (2019) · European Radiology · doi:10.1007/s00330-019-06163-2 · Cited by 250 · see citing works
Self-citationLearning tree-structured representation for 3D coronary artery segmentation
Kong, B., Wang, X., Bai, J., Lu, Y., Gao, F., Cao, K., Xia, J., Song, Q., & Yin, Y. (2019) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2019.101688 · Cited by 102 · see citing works
Self-citationDepeursinge, A., Chin, A. S.-L., Leung, A. N. C., Terrone, D. G., Bristow, M., Rosen, G., & Rubin, D. L. (2014) · Investigative Radiology · doi:10.1097/rli.0000000000000127 · Cited by 89 · see citing works
Xia, C., Li, X., Wang, X., Kong, B., Chen, Y., Yin, Y., Cao, K., Song, Q., Lyu, S., & Wu, X. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32245-8_64 · Cited by 21 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
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 · see citing works
Applications of digital technology in COVID-19 pandemic planning and response
Whitelaw, S., Mamas, M. A., Topol, E., & Van Spall, H. G. C. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30142-4 · Cited by 992 · see citing works
Yang, G., Ye, Q., & Xia, J. (2022) · Information Fusion · doi:10.1016/j.inffus.2021.07.016 · Cited by 717 · see citing works
Self-citationHarmon, S. A., Sanford, T. H., Xu, S., Turkbey, E. B., Roth, H., Xu, Z., Yang, D., Myronenko, A., Anderson, V., Amalou, A., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17971-2 · Cited by 587 · see citing works
Self-citationCOVID-19 Artificial Intelligence Diagnosis Using Only Cough Recordings
Laguarta, J., Hueto, F., & Subirana, B. (2020) · IEEE Open Journal of Engineering in Medicine and Biology · doi:10.1109/ojemb.2020.3026928 · Cited by 589 · see citing works
Machine-learning-based disease diagnosis: a comprehensive review
Ahsan, M. M., Luna, S. A., & Siddique, Z. (2022) · Healthcare · doi:10.3390/healthcare10030541 · Cited by 603 · see citing works
Mahmud, T., Rahman, M. A., & Fattah, S. A. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103869 · Cited by 525 · see citing works
A Noise-Robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions From CT Images
Wang, G., Liu, X., Li, C., Xu, Z., Ruan, J., Zhu, H., Meng, T., Li, K., Huang, N., & Zhang, S. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3000314 · Cited by 469 · see citing works
Self-citationCOVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data
Horry, M. J., Chakraborty, S., Paul, M., Ulhaq, A., Pradhan, B., Saha, M., & Shukla, N. (2020) · IEEE Access · doi:10.1109/access.2020.3016780 · Cited by 472 · see citing works
AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app
Imran, A., Posokhova, I., Qureshi, H. N., Masood, U., Riaz, M. S., Ali, K., John, C. N., Hussain, M. I., & Nabeel, M. (2020) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2020.100378 · Cited by 621 · see citing works
Digital technology use during <scp>COVID</scp> ‐19 pandemic: A rapid review
Vargo, D., Zhu, L., Benwell, B., & Yan, Z. (2021) · Human Behavior and Emerging Technologies · doi:10.1002/hbe2.242 · Cited by 459 · see citing works
Collaborative Federated Learning for Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge
Qayyum, A., Ahmad, K., Ahsan, M. A., Al-Fuqaha, A., & Qadir, J. (2022) · IEEE Open Journal of the Computer Society · doi:10.1109/ojcs.2022.3206407 · Cited by 345 · see citing works
Yang, D., Xu, Z., Li, W., Myronenko, A., Roth, H. R., Harmon, S., Xu, S., Turkbey, B., Turkbey, E., Wang, X., et al. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.101992 · Cited by 279 · see citing works
Self-citationFederated learning for COVID-19 screening from Chest X-ray images
Feki, I., Ammar, S., Kessentini, Y., & Muhammad, K. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107330 · Cited by 278 · see citing works
Singh, A., Kanaujia, A., Singh, V. K., & Vinuesa, R. (2024) · Sustainable Development · doi:10.1002/sd.2706 · Cited by 211 · see citing works
Wehbe, R. M., Sheng, J., Dutta, S., Chai, S., Dravid, A., Barutcu, S., Wu, Y., Cantrell, D. R., Xiao, N., Allen, B. D., et al. (2021) · Radiology · doi:10.1148/radiol.2020203511 · Cited by 190 · see citing works
The Use of Digital Health in the Detection and Management of COVID-19
Alwashmi, M. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17082906 · Cited by 159 · see citing works
COVID-19 image classification using deep learning: Advances, challenges and opportunities
Aggarwal, P., Mishra, N. K., Fatimah, B., Singh, P., Gupta, A., & Joshi, S. D. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105350 · Cited by 168 · see citing works
Eche, T., Schwartz, L. H., Mokrane, F.-Z., & Dercle, L. (2021) · Radiology: Artificial Intelligence · doi:10.1148/ryai.2021210097 · Cited by 163 · see citing works
A survey on deep transfer learning to edge computing for mitigating the COVID-19 pandemic
Sufian, A., Ghosh, A., Sadiq, A. S., & Smarandache, F. (2020) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2020.101830 · Cited by 156 · see citing works
Machine learning to assist clinical decision-making during the COVID-19 pandemic
Debnath, S., Barnaby, D. P., Coppa, K., Makhnevich, A., Kim, E. J., Chatterjee, S., Tóth, V., Levy, T. J., Paradis, M. D., Cohen, S. L., et al. (2020) · Bioelectronic Medicine · doi:10.1186/s42234-020-00050-8 · Cited by 104 · see citing works
GAN-Generated Faces Detection: A Survey and New Perspectives
Wang, X., Guo, H., Hu, S., Chang, M.-C., & Lyu, S. (2023) · Frontiers in Artificial Intelligence and Applications · doi:10.3233/faia230558 · Cited by 98 · see citing works
Self-citationA systematic review on AI/ML approaches against COVID-19 outbreak
Dogan, O., Tiwari, S., Jabbar, M. A., & Guggari, S. (2021) · Complex & Intelligent Systems · doi:10.1007/s40747-021-00424-8 · Cited by 87 · see citing works
Explainable AI for COVID-19 CT Classifiers: An Initial Comparison Study
Ye, Q., Xia, J., & Yang, G. (2021) · 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS) · doi:10.1109/cbms52027.2021.00103 · Cited by 83 · see citing works
Self-citationAdvancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence
Bai, X., Wang, H., Ma, L., Xu, Y., Gan, J., Fan, Z., Yang, F., Ma, K., Yang, J., Bai, S., et al. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00421-z · Cited by 75 · see citing works
Self-citationBlockchain-Federated-Learning and Deep Learning Models for COVID-19 Detection Using CT Imaging
Kumar, R., Khan, A. A., Kumar, J., Zakria, Golilarz, N. A., Zhang, S., Ting, Y., Zheng, C., & Wang, W. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3076767 · Cited by 57 · see citing works
Self-citationMallio, C. A., Napolitano, A., Castiello, G., Giordano, F. M., D'Alessio, P., Iozzino, M., Sun, Y., Angeletti, S., Russano, M., Santini, D., et al. (2021) · Cancers · doi:10.3390/cancers13040652 · Cited by 30 · see citing works
CDSS for Early Recognition of Respiratory Diseases Based on AI Techniques: A Systematic Review
Ali, S. W., Asif, M., Zia, M. Y. I., Rashid, M., Syed, S. A., & Nava, E. (2023) · Wireless Personal Communications · doi:10.1007/s11277-023-10432-1 · Cited by 13 · see citing works
Walston, S. L., Matsumoto, T., Miki, Y., & Ueda, D. (2022) · The British Journal of Radiology · doi:10.1259/bjr.20220058 · Cited by 9 · see citing works
Klingelhöfer, D., Braun, M., Dröge, J., Groneberg, D. A., & Brüggmann, D. (2025) · Globalization and Health · doi:10.1186/s12992-025-01128-1 · Cited by 6 · see citing works
- Authors
- Li, L., Qin, L., Xu, Z., Yin, Y., Wang, X., Kong, B., Bai, J., Lu, Y., Fang, Z., Song, Q., Cao, K., Liu, D., Wang, G., Xu, Q., Fang, X., Zhang, S., Xia, J., & Xia, J.
- Affiliations
- Jianghan University (CN)
- Chinese PLA General Hospital (CN)
- Corresponding author
- Xia, Jun
- ORCID
- Venue
- Radiology
- Published
- 2020-08
- Volume
- 296
- Issue
- 2
- Pages
- E65-E71
- ISSN
- 0033-8419 (ISSN-L)
- 1527-1315
- PMC
- PMC7233473
- OpenAlex
- W3011149445
- Cited by
- 1,927
- References
- 20
- Citations by year
- 2026: 31 · 2025: 80 · 2024: 132 · 2023: 216 · 2022: 410 · 2021: 686 · 2020: 370 · 2019: 1
- FWCI
- 204.81
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Radiology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- COVID-19 detection, community-acquired pneumonia, chest CT, deep learning, automated disease detection, diagnostic accuracy
- MeSH terms
- Betacoronavirus, Deep Learning, COVID-19, SARS-CoV-2, COVID-19 Testing, Adult, Aged, Artificial Intelligence, Diagnosis, Differential, Female, Humans, Male, Middle Aged, Pneumonia, Viral, Radiographic Image Interpretation, Computer-Assisted, Retrospective Studies, ROC Curve, Sensitivity and Specificity, Tomography, X-Ray Computed, Community-Acquired Infections, Coronavirus Infections, Clinical Laboratory Techniques, Imaging, Three-Dimensional, Pandemics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
