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ORIGINAL ARTICLE
Year : 2021  |  Volume : 8  |  Issue : 1  |  Page : 1-8

An artificial intelligence-based radiomics model for differential diagnosis between coronavirus disease 2019 and other viral pneumonias


1 Guizhou University; Department of Medical Imaging, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, NHC Key Laboratory of Pulmonary Immune-Related Diseases, Guizhou Provincial People's Hospital, Guiyang, Guizhou, China
2 Department of Medical Imaging, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, NHC Key Laboratory of Pulmonary Immune-Related Diseases, Guizhou Provincial People's Hospital, Guiyang, Guizhou, China
3 Guangzhou Women and Children Medical Center, Guangdong, China
4 Information and Network Management Center, Guizhou Provincial People's Hospital, Guiyang, Guizhou, China
5 AI Lab, Deepwise and League of PhD Technology Co. LTD, Beijing, China

Correspondence Address:
Dr. Rongpin Wang
Department of Medical Imaging, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, NHC Key Laboratory of Pulmonary Immune-related Diseases, Guizhou Provincial People's Hospital, Guiyang, Guizhou
China
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/RID.RID_1_21

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OBJECTIVE: To set up a differential diagnosis radiomics model to identify coronavirus disease 2019 (COVID-19) and other viral pneumonias based on an artificial intelligence (AI) approach that utilizes computed tomography (CT) images. MATERIALS AND METHODS: This retrospective multi-center research involved 225 patients with COVID-19 and 265 patients with other viral pneumonias. The least absolute shrinkage and selection operator algorithm was used for the optimized features selection from 1218 radiomics features. Finally, a logistic regression (LR) classifier was applied to construct different diagnosis models. The receiver operating characteristic curve analysis was applied to evaluate the accuracy of different models. RESULTS: The patients were divided into a training set (313 of 392, 80%), an internal test set (79 of 392, 20%) and an external test set (n = 98). Thirteen features were selected to build the machine learning-based CT radiomics models. LR classifiers performed well in the training set (area under the curve [AUC] = 0.91), internal test set (AUC = 0.94), and external test set (AUC = 0.91). Delong tests suggested there was no significant difference between training and the two test sets (P > 0.05). CONCLUSION: The use of an AI-based radiomics model enables rapid discrimination of patients with COVID-19 from other viral infections, which can aid better surveillance and control during a pneumonia outbreak.


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