An artificial intelligence system for predicting mortality in COVID-19 patients using chest X-rays: a retrospective study

  1. Avinash Nanivadekar
  2. Kapil Zirpe
  3. Ashutosh Dwivedi
  4. Rajan Patel
  5. Richa Pant
  6. Tanveer Gupte
  7. Rohit Lokwani
  8. Dinesh Shende
  9. Viraj Kulkarni
  10. Amit Kharat

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Abstract

Background

Early prediction of disease severity in COVID-19 patients is essential. Chest X-ray (CXR) is a faster, widely available, and less expensive imaging modality that may be useful in predicting mortality in COVID-19 patients. Artificial Intelligence (AI) may help expedite CXR reading times, and improve mortality prediction. We sought to develop and assess an artificial intelligence system that used chest X-rays and clinical parameters to predict mortality in COVID-19 patients.

Methods

A retrospective study was conducted in Ruby Hall Clinic, Pune, India. The study included patients who had a positive real-time reverse transcriptase-polymerase chain reaction (RT-PCR) test for COVID-19 and at least one available chest X-ray at the time of their initial presentation or admission. Features from CXR images and clinical parameters were used to train the Random Forest model.

Results

Clinical data from a total of 201 patients was assessed retrospectively. The average age of the cohort was 51.4±14.8 years, with 29.4% of the patients being over the age of 60. The model, which used CXRs and clinical parameters as inputs, had a sensitivity of 0.83 [95% CI: 0.7, 0.95] and a specificity of 0.7 [95% CI: 0.64, 0.77]. The area under the curve (AUC) on receiver operating characteristics (ROC) was increased from 0.74 [95% CI: 0.67, 0.8] to 0.79 [95% CI: 0.72, 0.85] when the model included features of CXRs in addition to clinical parameters.

Conclusion

An Artificial Intelligence (AI) model based on CXRs and clinical parameters demonstrated high sensitivity and can be used as a rapid and reliable tool for COVID-19 mortality prediction.

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    SciScore for 10.1101/2021.09.22.21263956: (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

    No key resources detected.

    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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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.

    Results from scite Reference Check: We found no unreliable references.

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  2. Version published to 10.1101/2021.09.22.21263956v1 on medRxiv