ISSN 1004-4140
CN 11-3017/P
Volume 32 Issue 3
May  2023
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WANG X L, ZHAO J H. Value of AI-based Multiomics Analysis in Differentiating COVID-19 from Community-acquired Pneumonia[J]. CT Theory and Applications, 2023, 32(3): 357-366. DOI: 10.15953/j.ctta.2023.049. (in Chinese).
Citation: WANG X L, ZHAO J H. Value of AI-based Multiomics Analysis in Differentiating COVID-19 from Community-acquired Pneumonia[J]. CT Theory and Applications, 2023, 32(3): 357-366. DOI: 10.15953/j.ctta.2023.049. (in Chinese).
shu

Value of AI-based Multiomics Analysis in Differentiating COVID-19 from Community-acquired Pneumonia

  • 1.

    Graduate School, Baotou Medical College, Inner Mongolia University of Science and Technology, Baotou 014040, China

  • 2.

    Department of Medical Imaging, Inner Mongolia People’s Hospital, Hohhot 010017, China

  • 3.

    Inner Mongolia Medical University Inner Mongolia Clinical Medical College/Baotou Medical College Inner Mongolia Clinical Medical College, Hohhot 010017, China

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  • Received Date: March 12, 2023
  • Accepted Date: April 22, 2023
  • Available Online: May 03, 2023
  • Published Date: May 30, 2023
    Graphical Abstract
    • Abstract
  • Objective: To assess the effectiveness of a multiomics model that combines radiomics characteristics and routine clinical information (including clinical symptoms and laboratory data) to distinguish between coronavirus disease 2019 (COVID-19) and community-acquired pneumonia (CAP). Methods: Retrospective data of patients with confirmed COVID-19 caused by the Omicron variant and patients with CAP caused by other viral infections were collected, including chest CT imaging and clinical data. Radiomics, clinical features, and multiomics models were constructed using the entire dataset, and the performance of each model in distinguishing between COVID-19 and CAP was evaluated using receiver operating characteristic curve (ROC) analysis. Results: A total of 8 radiomics features and 7 clinical features were selected to construct the radiomics, clinical features, and multiomics models. The area under the subject operating characteristic curve (AUC) of the radiomics model was 0.759, that of the clinical model was 0.853, and that of the multiomics model was 0.9. Conclusions: The study suggests that AI-based multiomics model has a better performance in differentiating between COVID-19 and CAP compared with those of the radiomics and clinical features models.
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    • References (22)
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