A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis

  1. Shuo Wang
  2. Yunfei Zha
  3. Weimin Li
  4. Qingxia Wu
  5. Xiaohu Li
  6. Meng Niu
  7. Meiyun Wang
  8. Xiaoming Qiu
  9. Hongjun Li
  10. He Yu
  11. Wei Gong
  12. Yan Bai
  13. Li Li
  14. Yongbei Zhu
  15. Liusu Wang
  16. Jie Tian

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Abstract

Coronavirus disease 2019 (COVID-19) has spread globally, and medical resources become insufficient in many regions. Fast diagnosis of COVID-19 and finding high-risk patients with worse prognosis for early prevention and medical resource optimisation is important. Here, we proposed a fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis by routinely used computed tomography.

We retrospectively collected 5372 patients with computed tomography images from seven cities or provinces. Firstly, 4106 patients with computed tomography images were used to pre-train the deep learning system, making it learn lung features. Following this, 1266 patients (924 with COVID-19 (471 had follow-up for >5 days) and 342 with other pneumonia) from six cities or provinces were enrolled to train and externally validate the performance of the deep learning system.

In the four external validation sets, the deep learning system achieved good performance in identifying COVID-19 from other pneumonia (AUC 0.87 and 0.88, respectively) and viral pneumonia (AUC 0.86). Moreover, the deep learning system succeeded to stratify patients into high- and low-risk groups whose hospital-stay time had significant difference (p=0.013 and p=0.014, respectively). Without human assistance, the deep learning system automatically focused on abnormal areas that showed consistent characteristics with reported radiological findings.

Deep learning provides a convenient tool for fast screening of COVID-19 and identifying potential high-risk patients, which may be helpful for medical resource optimisation and early prevention before patients show severe symptoms.

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  1. ScreenIT

    SciScore for 10.1101/2020.03.24.20042317: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The implementation of the DL system used the Keras 2.0.0 toolkit and Python 2.7.
    Python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Despite the good performance of the DL system, this study has several limitations. First, there are other prognostic end events such as death or admission to intensive care unit, and they were not considered in this study. Second, the management of severe and mild COVID-19 are different, thereby, explore prognosis of COVID-19 in these two groups separately should be helpful. Contributors: YZ and JT conceived and designed the study. SW implemented the DL system and wrote the paper. QW, YZ, and LW contributed to the data process and analysis. MN, HY, WG, YB, XQ, LL, XL, MW, HL, and WL contributed to data collection.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: Please consider improving the rainbow (“jet”) colormap(s) used on pages 26 and 27. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

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  2. Version published to 10.1183/13993003.00775-2020
  3. Version published to 10.1101/2020.03.24.20042317v1 on medRxiv