Value of AI-based Multiomics Analysis in Differentiating COVID-19 from Community-acquired Pneumonia
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: Retrospe...
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| Format: | Article |
| Language: | English |
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Editorial Office of Computerized Tomography Theory and Application
2023-05-01
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| Series: | CT Lilun yu yingyong yanjiu |
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| Online Access: | https://www.cttacn.org.cn/cn/article/doi/10.15953/j.ctta.2023.049 |
| _version_ | 1797810237170778112 |
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| author | Xiaolan WANG Jianhua ZHAO |
| author_facet | Xiaolan WANG Jianhua ZHAO |
| author_sort | Xiaolan WANG |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-13T07:06:15Z |
| format | Article |
| id | doaj.art-23844cfce6af461abc3cf5d4e9ad1f6f |
| institution | Directory Open Access Journal |
| issn | 1004-4140 |
| language | English |
| last_indexed | 2024-03-13T07:06:15Z |
| publishDate | 2023-05-01 |
| publisher | Editorial Office of Computerized Tomography Theory and Application |
| record_format | Article |
| series | CT Lilun yu yingyong yanjiu |
| spelling | doaj.art-23844cfce6af461abc3cf5d4e9ad1f6f2023-06-06T09:49:37ZengEditorial Office of Computerized Tomography Theory and ApplicationCT Lilun yu yingyong yanjiu1004-41402023-05-0132335736610.15953/j.ctta.2023.0492023.049Value of AI-based Multiomics Analysis in Differentiating COVID-19 from Community-acquired PneumoniaXiaolan WANG0Jianhua ZHAO1Graduate School, Baotou Medical College, Inner Mongolia University of Science and Technology, Baotou 014040, ChinaDepartment of Medical Imaging, Inner Mongolia People’s Hospital, Hohhot 010017, ChinaObjective: 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.https://www.cttacn.org.cn/cn/article/doi/10.15953/j.ctta.2023.049artificial intelligencecoronavirus disease 2019community-acquired pneumoniaradiomicsclinlabomics |
| spellingShingle | Xiaolan WANG Jianhua ZHAO Value of AI-based Multiomics Analysis in Differentiating COVID-19 from Community-acquired Pneumonia CT Lilun yu yingyong yanjiu artificial intelligence coronavirus disease 2019 community-acquired pneumonia radiomics clinlabomics |
| title | Value of AI-based Multiomics Analysis in Differentiating COVID-19 from Community-acquired Pneumonia |
| title_full | Value of AI-based Multiomics Analysis in Differentiating COVID-19 from Community-acquired Pneumonia |
| title_fullStr | Value of AI-based Multiomics Analysis in Differentiating COVID-19 from Community-acquired Pneumonia |
| title_full_unstemmed | Value of AI-based Multiomics Analysis in Differentiating COVID-19 from Community-acquired Pneumonia |
| title_short | Value of AI-based Multiomics Analysis in Differentiating COVID-19 from Community-acquired Pneumonia |
| title_sort | value of ai based multiomics analysis in differentiating covid 19 from community acquired pneumonia |
| topic | artificial intelligence coronavirus disease 2019 community-acquired pneumonia radiomics clinlabomics |
| url | https://www.cttacn.org.cn/cn/article/doi/10.15953/j.ctta.2023.049 |
| work_keys_str_mv | AT xiaolanwang valueofaibasedmultiomicsanalysisindifferentiatingcovid19fromcommunityacquiredpneumonia AT jianhuazhao valueofaibasedmultiomicsanalysisindifferentiatingcovid19fromcommunityacquiredpneumonia |