End-to-end automatic differentiation of the coronavirus disease 2019 (COVID-19) from viral pneumonia based on chest CT
Open Access
- 22 June 2020
- journal article
- research article
- Published by Springer Science and Business Media LLC in European Journal of Nuclear Medicine and Molecular Imaging
- Vol. 47 (11), 2516-2524
- https://doi.org/10.1007/s00259-020-04929-1
Abstract
Purpose In the absence of a virus nucleic acid real-time reverse transcriptase-polymerase chain reaction (RT-PCR) test and experienced radiologists, clinical diagnosis is challenging for viral pneumonia with clinical symptoms and CT signs similar to that of coronavirus disease 2019 (COVID-19). We developed an end-to-end automatic differentiation method based on CT images to identify COVID-19 pneumonia patients in real time. Methods From January 18 to February 23, 2020, we conducted a retrospective study and enrolled 201 patients from two hospitals in China who underwent chest CT and RT-PCR tests, of which 98 patients tested positive for COVID-19 (118 males and 83 females, with an average age of 42 years). Patient CT images from one hospital were divided among training, validation and test datasets with an 80%:10%:10% ratio. An end-to-end representation learning method using a large-scale bi-directional generative adversarial network (BigBiGAN) architecture was designed to extract semantic features from the CT images. The semantic feature matrix was input for linear classifier construction. Patients from the other hospital were used for external validation. Differentiation accuracy was evaluated using a receiver operating characteristic curve. Results Based on the 120-dimensional semantic features extracted by BigBiGAN from each image, the linear classifier results indicated that the area under the curve (AUC) in the training, validation and test datasets were 0.979, 0.968 and 0.972, respectively, with an average sensitivity of 92% and specificity of 91%. The AUC for external validation was 0.850, with a sensitivity of 80% and specificity of 75%. Publicly available architecture and computing resources were used throughout the study to ensure reproducibility. Conclusion This study provides an efficient recognition method for coronavirus disease 2019 pneumonia, using an end-to-end design to implement targeted and effective isolation for the containment of this communicable disease.Keywords
Funding Information
- Postdoctoral Research Foundation of China (2018M630310)
- China Scholarship Council (201908210051)
This publication has 28 references indexed in Scilit:
- Chest CT for Typical Coronavirus Disease 2019 (COVID-19) Pneumonia: Relationship to Negative RT-PCR TestingRadiology, 2020
- CT Manifestations of Two Cases of 2019 Novel Coronavirus (2019-nCoV) PneumoniaRadiology, 2020
- Emerging 2019 Novel Coronavirus (2019-nCoV) PneumoniaRadiology, 2020
- Wave-CAIPI susceptibility-weighted imaging achieves diagnostic performance comparable to conventional susceptibility-weighted imaging in half the scan timeEuropean Radiology, 2020
- A novel coronavirus outbreak of global health concernThe Lancet, 2020
- China coronavirus: WHO declares international emergency as death toll exceeds 200Published by BMJ ,2020
- Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCREurosurveillance, 2020
- Artificial intelligence in radiologyNature Reviews Cancer, 2018
- Comparison of Patients Hospitalized With Influenza A Subtypes H7N9, H5N1, and 2009 Pandemic H1N1Clinical Infectious Diseases, 2014