Int J Biol Sci 2021; 17(2):539-548. doi:10.7150/ijbs.53982

Research Paper

A rapid screening classifier for diagnosing COVID-19

Yang Xia1#, Weixiang Chen2#, Hongyi Ren3#, Jianping Zhao4#, Lihua Wang5#, Rui Jin1, Jiesen Zhou1, Qiyuan Wang5, Fugui Yan1, Bin Zhang1, Jian Lou1, Shaobin Wang1, Xiaomeng Li3, Jie Zhou2, Liming Xia6✉, Cheng Jin2✉, Jianjiang Feng2✉, Wen Li1✉, Huahao Shen1✉

1. Key Laboratory of Respiratory Disease of Zhejiang Province, Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
2. Department of Automation, Tsinghua University, Beijing, China.
3. Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.
4. Department of Respiratory Disease, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
5. Department of Radiology, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
6. Department of Radiology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
#Co-first authors.

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Citation:
Xia Y, Chen W, Ren H, Zhao J, Wang L, Jin R, Zhou J, Wang Q, Yan F, Zhang B, Lou J, Wang S, Li X, Zhou J, Xia L, Jin C, Feng J, Li W, Shen H. A rapid screening classifier for diagnosing COVID-19. Int J Biol Sci 2021; 17(2):539-548. doi:10.7150/ijbs.53982. Available from https://www.ijbs.com/v17p0539.htm

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Abstract

Rationale: Coronavirus disease 2019 (COVID-19) has caused a global pandemic. A classifier combining chest X-ray (CXR) with clinical features may serve as a rapid screening approach.

Methods: The study included 512 patients with COVID-19 and 106 with influenza A/B pneumonia. A deep neural network (DNN) was applied, and deep features derived from CXR and clinical findings formed fused features for diagnosis prediction.

Results: The clinical features of COVID-19 and influenza showed different patterns. Patients with COVID-19 experienced less fever, more diarrhea, and more salient hypercoagulability. Classifiers constructed using the clinical features or CXR had an area under the receiver operating curve (AUC) of 0.909 and 0.919, respectively. The diagnostic efficacy of the classifier combining the clinical features and CXR was dramatically improved and the AUC was 0.952 with 91.5% sensitivity and 81.2% specificity. Moreover, combined classifier was functional in both severe and non-serve COVID-19, with an AUC of 0.971 with 96.9% sensitivity in non-severe cases, which was on par with the computed tomography (CT)-based classifier, but had relatively inferior efficacy in severe cases compared to CT. In extension, we performed a reader study involving three experienced pulmonary physicians, artificial intelligence (AI) system demonstrated superiority in turn-around time and diagnostic accuracy compared with experienced pulmonary physicians.

Conclusions: The classifier constructed using clinical and CXR features is efficient, economical, and radiation safe for distinguishing COVID-19 from influenza A/B pneumonia, serving as an ideal rapid screening tool during the COVID-19 pandemic.

Keywords: COVID-19, chest X-ray, clinical feature, deep learning