Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 592

The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.

Development and evaluation of an artificial intelligence system for COVID-19 diagnosis

Open access
Jin, Cheng, Chen, Weixiang, Cao, Yukun, Xu, Zhanwei, Tan, Zimeng, Zhang, Xin, Deng, Lei, Zheng, Chuansheng, Zhou, Jie, Shi, Heshui, Feng, Jianjiang·2020·Nature Communications
DOI 10.1038/s41467-020-18685-1

Published version of a preprint

Open this paper in CoChat — free account

Abstract

Early detection of COVID-19 based on chest CT enables timely treatment of patients and helps control the spread of the disease. We proposed an artificial intelligence (AI) system for rapid COVID-19 detection and performed extensive statistical analysis of CTs of COVID-19 based on the AI system. We developed and evaluated our system on a large dataset with more than 10 thousand CT volumes from COVID-19, influenza-A/B, non-viral community acquired pneumonia (CAP) and non-pneumonia subjects. In such a difficult multi-class diagnosis task, our deep convolutional neural network-based system is able to achieve an area under the receiver operating characteristic curve (AUC) of 97.81% for multi-way classification on test cohort of 3,199 scans, AUC of 92.99% and 93.25% on two publicly available datasets, CC-CCII and MosMedData respectively. In a reader study involving five radiologists, the AI system outperforms all of radiologists in more challenging tasks at a speed of two orders of magnitude above them. Diagnosis performance of chest x-ray (CXR) is compared to that of CT. Detailed interpretation of deep network is also performed to relate system outputs with CT presentations. The code is available at https://github.com/ChenWWWeixiang/diagnosis_covid19 .

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41467-020-18685-1

Cites

20 of 20 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

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

Show all 20 references

Research works printed but not linked: 27

  1. Bai, H. X. et al. Performance of radiologists in differentiating COVID-19 from viral pneumonia on chest CT. Radiology 296, E46–E54 (2020). · DOI · Paper page
  2. Ai, T. et al. Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology 296, E32–E40 (2020). · DOI · Paper page
  3. Rubin, G. D. et al. The role of chest imaging in patient management during the COVID-19 pandemic: a multinational consensus statement from the Fleischner Society. Chest 158, 106–116 (2020). · DOI · Paper page
  4. Wong, H. Y. F. et al. Frequency and distribution of chest radiographic findings in COVID-19 positive patients. Radiology 296, E72–E78 (2020). · DOI · Paper page
  5. Shi, H. et al. Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study. Lancet Infect. Dis. 20, 425–434 (2020). · DOI · Paper page
  6. Qin, Z. Z. et al. Using artificial intelligence to read chest radiographs for tuberculosis detection: a multi-site evaluation of the diagnostic accuracy of three deep learning systems. Sci. Rep. 9, 1–10 (2019). · DOI · Paper page
  7. Xie, H., Yang, D., Sun, N., Chen, Z. & Zhang, Y. Automated pulmonary nodule detection in CT images using deep convolutional neural networks. Pattern Recognit 85, 109–119 (2019). · DOI · Paper page
  8. Liao, F., Liang, M., Li, Z., Hu, X. & Song, S. Evaluate the malignancy of pulmonary nodules using the 3-d deep leaky noisy-or network. IEEE Trans. neural Netw. Learn. Syst. 30, 3484–3495 (2019). · DOI · Paper page
  9. Zhu, W., Liu, C., Fan, W. & Xie, X. Deeplung: deep 3d dual path nets for automated pulmonary nodule detection and classification. In 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 673–681 (IEEE, 2018). · DOI · Paper page
  10. Nash, M. et al. Deep learning, computer-aided radiography reading for tuberculosis: a diagnostic accuracy study from a tertiary hospital in India. Sci. Rep. 10, 1–10 (2020). · DOI · Paper page
  11. Dong, D. et al. The role of imaging in the detection and management of COVID-19: a review. IEEE Rev. Biomed. Eng. (2020). · DOI · Paper page
  12. Wang, X. et al. A weakly-supervised framework for COVID-19 Classification and lesion localization from chest CT. IEEE Trans. Med. Imaging 39, 2615–2625 (2020). · DOI · Paper page
  13. Han, Z. et al. Accurate screening of COVID-19 using attention based deep 3D multiple instance learning. IIEEE Trans. Med. Imaging 39, 2584–2594 (2020). · DOI · Paper page
  14. Ouyang, X. et al. Dual-sampling attention network for diagnosis of COVID-19 from community acquired pneumonia. IEEE Trans. Med. Imaging 39, 2595–2605 (2020). · DOI · Paper page
  15. Murphy, K. et al. COVID-19 on the chest radiograph: a multi-reader evaluation of an AI system. Radiology 296, E166–E172 (2020). · DOI · Paper page
  16. Armato, S. G. III et al. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans. Med. Phys. 38, 915–931 (2011). · DOI · Paper page
  17. Morozov, S. et al. MosMedData: chest CT scans with COVID-19 related findings. Preprint at https://arxiv.org/abs/2005.06465 (2020). · arXiv · doi:10.1101/2020.05.20.20100362
  18. Maaten, Lvd & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).
  19. Van Griethuysen, J. J. et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 77, e104–e107 (2017). · doi:10.1158/0008-5472.can-17-0339
  20. Bernheim, A. et al. Chest CT findings in coronavirus disease-19 (COVID-19): relationship to duration of infection. Radiology 295, 200463 (2020). · DOI · Paper page
  21. Kanne, J. P. Chest CT Findings nn 2019 Novel Coronavirus (2019-nCoV) Infections from Wuhan, China: Key Points for the Radiologist. Report No. 0033-8419, 16–17 (Radiological Society of North America, 2020). · DOI · Paper page
  22. Paszke, A. et al. PyTorch: an imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems, 8024–8035 (NeurIPS, 2019).
  23. Dail, D. H. & Hammar, S. P. Dail and Hammar’s Pulmonary Pathology (Springer Science & Business Media, 2013).
  24. Cotes, J. E., Chinn, D. J. & Miller, M. R. Lung Function: Physiology, Measurement and Application in Medicine (Wiley, 2009).
  25. Panagiotou, M., Church, A. C., Johnson, M. K. & Peacock, A. J. Pulmonary vascular and cardiac impairment in interstitial lung disease. Eur. Respir. Rev. 26, 160053 (2017). · DOI
  26. Breslin, J. W. et al. Lymphatic vessel network structure and physiology. Compr. Physiol. 9, 207–299 (2011).
  27. Moldoveanu, B. et al. Inflammatory mechanisms in the lung. J. Inflamm. Res. 2, 1 (2009).

Cited by

See all 592

Metadata

Authors
Jin, C., Chen, W., Cao, Y., Xu, Z., Tan, Z., Zhang, X., Deng, L., Zheng, C., Zhou, J., Shi, H., & Feng, J.
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2020-10-09
Volume
11
Issue
1
Pages
5088-5088
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-020-18685-1
OpenAlex
W3091940685
Cited by
592
References
20
FWCI
54.69
Type
journal article
Language
en
Open access
gold
Licence

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

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Development and evaluation of an artificial intelligence system for COVID-19 diagnosis. https://app.cochat.ai/papers/10.1038/s41467-020-18685-1
The paper
Jin, C., Chen, W., Cao, Y., Xu, Z., Tan, Z., Zhang, X., Deng, L., Zheng, C., Zhou, J., Shi, H., & Feng, J. (2020). Development and evaluation of an artificial intelligence system for COVID-19 diagnosis. Nature Communications. https://doi.org/10.1038/s41467-020-18685-1

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.