A diagnostic model for coronavirus disease 2019 (COVID-19) based on radiological semantic and clinical features: a multi-center study
Detection Principle
Imaging-CT scan
Target
1745 lesions and 67 clinical features
Testing Method Category
In_silico
Testing Method
To identify differences in CT imaging and clinical manifestations between pneumonia patients with and without COVID-19, and to develop and validate a diagnostic model for COVID-19 based on radiological semantic and clinical features alone.
Testing Method - Additional Info
Reported Performance
Remark: Three diagnostic models achieved an area under the curve value as high as 0.986 (95% CI 0.966~1.000). The clinical and radiological semantic models provided a better diagnostic performance and more considerable net benefits.
Sample Size
70 patients with covid and 66 patients with pneumonia
Assay Type
Peer-reviewed
yes
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