Assisting Scalable Diagnosis Automatically via CT Images in the Combat against COVID-19
Introductory paragraph The pandemic of coronavirus Disease 2019 (COVID-19) caused enormous loss of life globally. 1-3 Case identification is critical. The reference method is using real-time reverse transcription PCR (rRT-PCR) assays, with limitations that may curb its prompt large-scale application. COVID-19 manifests with chest computed tomography (CT) abnormalities, some even before the onset of symptoms. We tested the hypothesis that application of deep learning (DL) to the 3D CT images could help identify COVID-19 infections. Using the data from 920 COVID-19 and 1,073 non-COVID-19 pneumonia patients, we developed a modified DenseNet-264 model, COVIDNet, to classify CT images to either class. When tested on an independent set of 233 COVID-19 and 289 non-COVID-19 patients. COVIDNet achieved an accuracy rate of 94.3% and an area under the curve (AUC) of 0.98. Application of DL to CT images may improve both the efficiency and capacity of case detection and long-term surveillance..
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Preprint| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
bioRxiv.org - (2022) vom: 23. Okt. Zur Gesamtaufnahme - year:2022 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Liu, Bohan [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| Themen: |
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| doi: |
10.1101/2020.05.11.20093732 |
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| PPN (Katalog-ID): |
XBI017912040 |
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| 245 | 1 | 0 | |a Assisting Scalable Diagnosis Automatically via CT Images in the Combat against COVID-19 |
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| 520 | |a Introductory paragraph The pandemic of coronavirus Disease 2019 (COVID-19) caused enormous loss of life globally. 1-3 Case identification is critical. The reference method is using real-time reverse transcription PCR (rRT-PCR) assays, with limitations that may curb its prompt large-scale application. COVID-19 manifests with chest computed tomography (CT) abnormalities, some even before the onset of symptoms. We tested the hypothesis that application of deep learning (DL) to the 3D CT images could help identify COVID-19 infections. Using the data from 920 COVID-19 and 1,073 non-COVID-19 pneumonia patients, we developed a modified DenseNet-264 model, COVIDNet, to classify CT images to either class. When tested on an independent set of 233 COVID-19 and 289 non-COVID-19 patients. COVIDNet achieved an accuracy rate of 94.3% and an area under the curve (AUC) of 0.98. Application of DL to CT images may improve both the efficiency and capacity of case detection and long-term surveillance. | ||
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| 700 | 1 | |a Xie, Peng |e verfasserin |4 aut | |
| 700 | 1 | |a Tan, Yiqing |e verfasserin |4 aut | |
| 700 | 1 | |a Du, Jicheng |e verfasserin |4 aut | |
| 700 | 1 | |a Shan, Wei |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Chenghui |e verfasserin |4 aut | |
| 700 | 1 | |a Zhong, Qin |e verfasserin |4 aut | |
| 700 | 1 | |a Lin, Xixiang |e verfasserin |4 aut | |
| 700 | 1 | |a Guan, Xizhou |e verfasserin |4 aut | |
| 700 | 1 | |a Xing, Ning |e verfasserin |4 aut | |
| 700 | 1 | |a Sun, Yuhui |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Wenjun |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Zhibing |e verfasserin |4 aut | |
| 700 | 1 | |a Fu, Xia |e verfasserin |4 aut | |
| 700 | 1 | |a Fan, Yanqing |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Meifang |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Na |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Lin |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Yaou |e verfasserin |4 aut | |
| 700 | 1 | |a Xu, Lin |e verfasserin |4 aut | |
| 700 | 1 | |a Du, Jingbo |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Zhenhua |e verfasserin |4 aut | |
| 700 | 1 | |a Hu, Xuelong |e verfasserin |4 aut | |
| 700 | 1 | |a Fan, Weipeng |e verfasserin |4 aut | |
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| 700 | 1 | |a Wu, Chongchong |e verfasserin |4 aut | |
| 700 | 1 | |a Nie, Yongkang |e verfasserin |4 aut | |
| 700 | 1 | |a Cheng, Liuquan |e verfasserin |4 aut | |
| 700 | 1 | |a Ma, Lin |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Zongren |e verfasserin |4 aut | |
| 700 | 1 | |a Jia, Qian |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Minchao |e verfasserin |4 aut | |
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| 700 | 1 | |a Li, Hao |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Jianxin |e verfasserin |4 aut | |
| 700 | 1 | |a He, Kunlun |e verfasserin |4 aut | |
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