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Cited by 675

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 access
Chen, Jun, Wu, Lianlian, Zhang, Jun, Zhang, Liang, Gong, Dexin, Zhao, Yilin, Chen, Qiuxiang, Huang, Shulan, Yang, Ming, Yang, Xiao, Hu, Shan, Wang, Yonggui, Hu, Xiao, Zheng, Biqing, Zhang, Kuo, Wu, Huiling, Dong, Zehua, Xu, Youming, Zhu, Yijie, Chen, Xi, Zhang, Mengjiao, Yu, Lilei, Cheng, Fan, Yu, Honggang·2020·Scientific Reports
DOI 10.1038/s41598-020-76282-0

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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.

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Show all 9 references

Research works printed but not linked: 16

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Cited by

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
Publisher
Springer Science and Business Media LLC
Published
2020-11-05
Volume
10
Issue
1
Pages
19196-19196
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-020-76282-0
OpenAlex
W3096956107
Cited by
675
References
9
FWCI
64.21
Type
journal article
Language
en
Open access
gold
Licence
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)

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