The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography
Open accessPublished version of a preprint
Abstract
Computed tomography (CT) is the preferred imaging method for diagnosing 2019 novel coronavirus (COVID19) pneumonia. We aimed to construct a system based on deep learning for detecting COVID-19 pneumonia on high resolution CT. For model development and validation, 46,096 anonymous images from 106 admitted patients, including 51 patients of laboratory confirmed COVID-19 pneumonia and 55 control patients of other diseases in Renmin Hospital of Wuhan University were retrospectively collected. Twenty-seven prospective consecutive patients in Renmin Hospital of Wuhan University were collected to evaluate the efficiency of radiologists against 2019-CoV pneumonia with that of the model. An external test was conducted in Qianjiang Central Hospital to estimate the system’s robustness. The model achieved a per-patient accuracy of 95.24% and a per-image accuracy of 98.85% in internal retrospective dataset. For 27 internal prospective patients, the system achieved a comparable performance to that of expert radiologist. In external dataset, it achieved an accuracy of 96%. With the assistance of the model, the reading time of radiologists was greatly decreased by 65%. The deep learning model showed a comparable performance with expert radiologist, and greatly improved the efficiency of radiologists in clinical practice.
Overview
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
Verification
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.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
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
Self-citationChen, 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
Self-citationFirst 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
Hui, D. S., I Azhar, E., Madani, T. A., Ntoumi, F., Kock, R., Dar, O., Ippolito, G., Mchugh, T. D., Memish, Z. A., Drosten, C., et al. (2020) · International Journal of Infectious Diseases · doi:10.1016/j.ijid.2020.01.009 · Cited by 3,809
Ardila, D., Kiraly, A. P., Bharadwaj, S., Choi, B., Reicher, J. J., Peng, L., Tse, D., Etemadi, M., Ye, W., Corrado, G., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0447-x · Cited by 2,167
Gong, D., Wu, L., Zhang, J., Mu, G., Shen, L., Liu, J., Wang, Z., Zhou, W., An, P., Huang, X., et al. (2020) · The Lancet Gastroenterology & Hepatology · doi:10.1016/s2468-1253(19)30413-3 · Cited by 393
Self-citationChae, K. J., Jin, G. Y., Ko, S. B., Wang, Y., Zhang, H., Choi, E. J., & Choi, H. (2020) · Academic Radiology · doi:10.1016/j.acra.2019.05.018 · Cited by 40
Self-citation
Research works printed but not linked: 16
- Rothe, C. et al. Transmission of COVID-19 Infection from an asymptomatic contact in Germany. N. Engl. J. Med. 382(10), 970–971 (2020). · DOI · Paper page
- Guan, H. et al. Clinical and thin-section CT features of patients with the COVID-19 in Wuhan (in Chinese). Radiol. Pract. 35, 125–130 (2020).
- China Ministry of Health. China Health Statistical Yearbook 2018[in Chinese]. 34–42 (Beijing: Peking Union Medical College Press, 2018).
- Liu, Y., Liu, G. & Zhang, Q. Deep learning and medical diagnosis. Lancet 394(10210), 1709–1710 (2019). · DOI
- Wu, L. et al. Randomised controlled trial of WISENSE, a real-time quality improving system for monitoring blind spots during esophagogastroduodenoscopy. Gut 68(12), 2161–2169 (2019). · DOI · Paper page
- Chen, D. et al. Comparing blind spots of unsedated ultrafine, sedated, and unsedated conventional gastroscopy with and without artificial intelligence: a prospective, single-blind, 3-parallel-group, randomized, single-center trial. Gastrointest. Endosc. 91(2), 332–339 (2020). · DOI
- Zhou, J. et al. A novel artificial intelligence system for the assessment of bowel preparation (with video). Gastrointest. Endosc. 91(2), 428–435 (2020). · DOI
- Wu, L. et al. A deep neural network improves endoscopic detection of early gastric cancer without blind spots. Endoscopy 51(06), 522–531 (2019). · DOI · Paper page
- Zhou, Z., Siddiquee, M. M. R., Tajbakhsh, N. & Liang, J. Unet++: a nested u-net architecture for medical image segmentation. Preprint at. https://arxiv.org/abs/1807.10165 (2018). · DOI · Paper page
- Chen, L., Papandreou, G., Schroff, F. & Adam, H. Rethinking atrous convolution for semantic image segmentation. Preprint at. https://arxiv.org/abs/1706.05587 (2017). · arXiv
- Cheng, V. C., Wong, S., To, K. K., Ho, P. L. & Yuen, K. Preparedness and proactive infection control measures against the emerging Wuhan coronavirus pneumonia in China. J. Hosp. Infect. 104(3), 254–255 (2020). · DOI
- Tang, B. et al. Estimation of the transmission risk of the 2019-nCoV and its implication for public health interventions. J. Clin. Med. 9(2), 462 (2020). · DOI · Paper page
- Riou, J. & Althaus, C. L. Pattern of early human-to-human transmission of Wuhan 2019 novel coronavirus (2019-nCoV), December 2019 to January 2020. Euro Surveill. 25(4), 2000058 (2020). · DOI · Paper page
- Prasse, B., Achterberg, M. A., Ma, L. & Van Mieghem, P. Network-based prediction of the 2019-nCoV epidemic outbreak in the Chinese Province Hubei. Preprint at. https://arxiv.org/abs/2002.04482 (2020). · DOI
- Boldog, P., Tekeli, T., Vizi, Z., Denes, A., Bartha, F. A. & Rost, G. Risk assessment of novel coronavirus 2019-nCoV outbreaks outside China. Preprint at. https://www.medrxiv.org/ (2020). · DOI
- Chen, Z. et al. Diagnosis and treatment recommendations for pediatric respiratory infection caused by the 2019 novel coronavirus. World J. Pediatr.. https://doi.org/10.1007/s12519-020-00345-5 (2020). · DOI · Paper page
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
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
Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays
Brunese, L., Mercaldo, F., Reginelli, A., & Santone, A. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105608 · Cited by 623
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
Metadata
- Authors
- Chen, J., Wu, L., Zhang, J., Zhang, L., Gong, D., Zhao, Y., Chen, Q., Huang, S., Yang, M., Yang, X., Hu, S., Wang, Y., Hu, X., Zheng, B., Zhang, K., Wu, H., Dong, Z., Xu, Y., Zhu, Y., Chen, X., Zhang, M., Yu, L., Cheng, F., & Yu, H.
- Venue
- Scientific Reports
- Published
- 2020-11-05
- Volume
- 10
- Issue
- 1
- Pages
- 19196-19196
- ISSN
- 2045-2322 (ISSN-L)
- OpenAlex
- W3096956107
- Cited by
- 675
- References
- 9
- FWCI
- 64.21
- Type
- journal article
- Language
- en
- Open access
- gold
- Licence
- creativecommons.org/licenses/by/4.0 (tdm)
- creativecommons.org/licenses/by/4.0 (vor)
- Funders
- Project of Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision (2018BCC337)
- Hubei Province Major Science and Technology Innovation Project (2018-916-000-008)
- the National Natural Science Foundation of China (81672387)
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
