From community-acquired pneumonia to COVID-19: a deep learning–based method for quantitative analysis of COVID-19 on thick-section CT scans

  1. Zhang Li
  2. Zheng Zhong
  3. Yang Li
  4. Tianyu Zhang
  5. Liangxin Gao
  6. Dakai Jin
  7. Yue Sun
  8. Xianghua Ye
  9. Li Yu
  10. Zheyu Hu
  11. Jing Xiao
  12. Lingyun Huang
  13. Yuling Tang

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Abstract

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  1. Version published to 10.1007/s00330-020-07042-x
  2. ScreenIT

    SciScore for 10.1101/2020.04.17.20070219: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: 2.1 Patients: This study was approved by the Ethics of Committees of the First Hospital of Changsha, Hunan, China.
    Consent: Informed consent for this retrospective study was waived.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    2.5 Statistical Analysis and Evaluation Metrics: Statistical analysis was performed by SAS (version 9.4) and Matlab (version 2018b).
    Matlab
    suggested: (MATLAB, RRID:SCR_001622)

    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:
    This study has several limitations. Firstly, we only evaluated changes of imaging bio-markers at the whole lung level in certain phrase. Although our model can compute the bio-markers at the lobe level, the standard phrases from the radiology reports were mostly at the whole lung level. Furthermore, some phrases in the reports like ‘lesion absorption’ might respond to either infection region decreasing or HU value reduction. Thus it needs more sophisticated and precise analysis evaluating our model in the future. Secondly, motion artifacts due to respiration and heart motion may cause false positive segmentation in the AI system. We noticed that some false positive segmentation affected the longitudinal infection evaluations6. One possible solution is to identity the motion artifacts before applying the infection segmenting. Finally, our model was only tested the COVID-19 positive patients. A recent study has shown that a deep learning based AI classification model can detect the COVID-19 and distinguish it from the community acquired pneumonia and other non-pneumonic lung diseases using thin-section HRCT [5]. As the next step, it would be interesting to see if our model can also differentiate the pneumonia caused by COVID-19 and other factors using the thick-section CT imaging. In conclusion, a deep learning based AI system is developed to quantify COVID-19 abnormal lung patterns, assess the disease severity and the progression using thick-section chest CT images. The imagin...

    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: We did not find any issues relating to colormaps.

    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

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2020.04.17.20070219v1 on medRxiv