An artificial intelligence-based radiomics model for differential diagnosis between coronavirus disease 2019 and other viral pneumonias
Radiology of Infectious Diseases
; 8(1):1-8, 2021.
Article
in English
| ProQuest Central | ID: covidwho-2119120
ABSTRACT
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 ANDMETHODS:
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.
Full text:
Available
Collection:
Databases of international organizations
Database:
ProQuest Central
Type of study:
Diagnostic study
Language:
English
Journal:
Radiology of Infectious Diseases
Year:
2021
Document Type:
Article
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