tetano
Editor, Senior Moderator
Eur J Nucl Med Mol Imaging
. 2020 Jun 22.
doi: 10.1007/s00259-020-04929-1. Online ahead of print.
End-to-end Automatic Differentiation of the Coronavirus Disease 2019 (COVID-19) From Viral Pneumonia Based on Chest CT
Jiangdian Song[SUP] 1 2 [/SUP], Hongmei Wang[SUP] 3 [/SUP], Yuchan Liu[SUP] 4 [/SUP], Wenqing Wu[SUP] 4 [/SUP], Gang Dai[SUP] 3 [/SUP], Zongshan Wu[SUP] 5 [/SUP], Puhe Zhu[SUP] 5 [/SUP], Wei Zhang[SUP] 5 [/SUP], Kristen W Yeom[SUP] 2 [/SUP], Kexue Deng[SUP] 6 [/SUP]
Affiliations
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: Artificial intelligence; BigBiGAN; Coronavirus disease 2019 pneumonia; Differentiation; Semantic features.
. 2020 Jun 22.
doi: 10.1007/s00259-020-04929-1. Online ahead of print.
End-to-end Automatic Differentiation of the Coronavirus Disease 2019 (COVID-19) From Viral Pneumonia Based on Chest CT
Jiangdian Song[SUP] 1 2 [/SUP], Hongmei Wang[SUP] 3 [/SUP], Yuchan Liu[SUP] 4 [/SUP], Wenqing Wu[SUP] 4 [/SUP], Gang Dai[SUP] 3 [/SUP], Zongshan Wu[SUP] 5 [/SUP], Puhe Zhu[SUP] 5 [/SUP], Wei Zhang[SUP] 5 [/SUP], Kristen W Yeom[SUP] 2 [/SUP], Kexue Deng[SUP] 6 [/SUP]
Affiliations
- PMID: 32567006
- DOI: 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: Artificial intelligence; BigBiGAN; Coronavirus disease 2019 pneumonia; Differentiation; Semantic features.