Dataset Information

Assisting scalable diagnosis automatically via CT images in the combat against COVID-19.

ABSTRACT: The pandemic of Coronavirus Disease 2019 (COVID-19) is causing enormous loss of life globally. Prompt case identification is critical. The reference method is the real-time reverse transcription PCR (RT-PCR) assay, whose limitations 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 the application of deep learning (DL) to 3D CT images could help identify COVID-19 infections. Using 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 pneumonia patients, COVIDNet achieved an accuracy rate of 94.3% and an area under the curve of 0.98. As of March 23, 2020, the COVIDNet system had been used 11,966 times with a sensitivity of 91.12% and a specificity of 88.50% in six hospitals with PCR confirmation. Application of DL to CT images may improve both efficiency and capacity of case detection and long-term surveillance.

SUBMITTER: Liu B

PROVIDER: S-EPMC7892869 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Assisting scalable diagnosis automatically via CT images in the combat against COVID-19.

Liu Bohan B Liu Pan P Dai Lutao L Yang Yanlin Y Xie Peng P Tan Yiqing Y Du Jicheng J Shan Wei W Zhao Chenghui C Zhong Qin Q Lin Xixiang X Guan Xizhou X Xing Ning N Sun Yuhui Y Wang Wenjun W Zhang Zhibing Z Fu Xia X Fan Yanqing Y Li Meifang M Zhang Na N Li Lin L Liu Yaou Y Xu Lin L Du Jingbo J Zhao Zhenhua Z Hu Xuelong X Fan Weipeng W Wang Rongpin R Wu Chongchong C Nie Yongkang Y Cheng Liuquan L Ma Lin L Li Zongren Z Jia Qian Q Liu Minchao M Guo Huayuan H Huang Gao G Shen Haipeng H Zhang Liang L Zhang Peifang P Guo Gang G Li Hao H An Weimin W Zhou Jianxin J He Kunlun K

Scientific reports 20210218 1

The pandemic of Coronavirus Disease 2019 (COVID-19) is causing enormous loss of life globally. Prompt case identification is critical. The reference method is the real-time reverse transcription PCR (RT-PCR) assay, whose limitations 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 the application of deep learning (DL) to 3D CT images could help identify COVID-19 ...[more]

PMID: 33603047

Similar Datasets

Project description:The recent global outbreak and spread of coronavirus disease (COVID-19) makes it an imperative to develop accurate and efficient diagnostic tools for the disease as medical resources are getting increasingly constrained. Artificial intelligence (AI)-aided tools have exhibited desirable potential; for example, chest computed tomography (CT) has been demonstrated to play a major role in the diagnosis and evaluation of COVID-19. However, developing a CT-based AI diagnostic system for the disease detection has faced considerable challenges, which is mainly due to the lack of adequate manually-delineated samples for training, as well as the requirement of sufficient sensitivity to subtle lesions in the early infection stages. In this study, we developed a dual-branch combination network (DCN) for COVID-19 diagnosis that can simultaneously achieve individual-level classification and lesion segmentation. To focus the classification branch more intensively on the lesion areas, a novel lesion attention module was developed to integrate the intermediate segmentation results. Furthermore, to manage the potential influence of different imaging parameters from individual facilities, a slice probability mapping method was proposed to learn the transformation from slice-level to individual-level classification. We conducted experiments on a large dataset of 1202 subjects from ten institutes in China. The results demonstrated that 1) the proposed DCN attained a classification accuracy of 96.74% on the internal dataset and 92.87% on the external validation dataset, thereby outperforming other models; 2) DCN obtained comparable performance with fewer samples and exhibited higher sensitivity, especially in subtle lesion detection; and 3) DCN provided good interpretability on the loci of infection compared to other deep models due to its classification guided by high-level semantic information. An online CT-based diagnostic platform for COVID-19 derived from our proposed framework is now available.

Dataset Information

Assisting scalable diagnosis automatically via CT images in the combat against COVID-19.

Publications

Assisting scalable diagnosis automatically via CT images in the combat against COVID-19.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets