AI-based analysis of CT images for rapid triage of COVID-19 patients

  1. Qinmei Xu
  2. Xianghao Zhan
  3. Zhen Zhou
  4. Yiheng Li
  5. Peiyi Xie
  6. Shu Zhang
  7. Xiuli Li
  8. Yizhou Yu
  9. Changsheng Zhou
  10. Longjiang Zhang
  11. Olivier Gevaert
  12. Guangming Lu

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Abstract

The COVID-19 pandemic overwhelms the medical resources in the stressed intensive care unit (ICU) capacity and the shortage of mechanical ventilation (MV). We performed CT-based analysis combined with electronic health records and clinical laboratory results on Cohort 1 ( n  = 1662 from 17 hospitals) with prognostic estimation for the rapid stratification of PCR confirmed COVID-19 patients. These models, validated on Cohort 2 ( n  = 700) and Cohort 3 ( n  = 662) constructed from nine external hospitals, achieved satisfying performance for predicting ICU, MV, and death of COVID-19 patients (AUROC 0.916, 0.919, and 0.853), even on events happened two days later after admission (AUROC 0.919, 0.943, and 0.856). Both clinical and image features showed complementary roles in prediction and provided accurate estimates to the time of progression ( p  < 0.001). Our findings are valuable for optimizing the use of medical resources in the COVID-19 pandemic. The models are available here: https://github.com/terryli710/COVID_19_Rapid_Triage_Risk_Predictor .

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  1. Version published to 10.1038/s41746-021-00446-z
  2. ScreenIT

    SciScore for 10.1101/2020.11.04.20225797: (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
    Statistical analysis: SPSS v15.0 [Chicago, SPSS Inc.] and MedCalc statistical software were used for statistical analysis.
    SPSS
    suggested: (SPSS, RRID:SCR_002865)
    MedCalc
    suggested: (MedCalc, RRID:SCR_015044)

    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:
    Our work has several limitations. First, we did not consider the effect of different treatments on the prognosis of patients among clinical centers. In our study, several treatments were adopted including oxygen therapy, MV, ECMO, antiviral treatment, antibiotic treatment, glucocorticoids, and intravenous immunoglobulin therapy. In-depth comparison of different treatment outcomes might improve response prediction. Second, ten well-experienced thoracic radiologists analyzed the CT images in consensus and evaluated traditional imaging features in our study, however, we did not study inter-reader variability and such an analysis might need to be addressed in future work. In addition, although our study had a large sample size with clear prognosis information, the numbers of endpoints were limited and only from Chinese hospitals which could potentially limit the generalizability of models in other areas. Finally, additional validation across populations from European and American hospitals are needed to further validate the reported models. In conclusion, we developed computational models with clinical prognostic estimation functions incorporating CT-based radiomics features as well as clinical data from electronic medical records for COVID-19 patients. This information may aid in delivering proper treatment and optimizing the use of limited medical resources in the current pandemic of COVID-19.

    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.

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  3. Version published to 10.1101/2020.11.04.20225797v1 on medRxiv