Application of deep learning-based diagnostic systems in screening asymptomatic COVID-19 patients among oversea returnees

Authors

  • Dawei Dong Department of Radiology, Beijing Xiaotangshan Hospital, Beijing, China
  • Zujin Luo Department of Respiratory and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
  • Yue Zheng Intensive Care Unit, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
  • Ying Liang Department of Radiology, Beijing Xiaotangshan Hospital, Beijing, China
  • Pengfei Zhao Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
  • Linlin Feng Department of Radiology, Beijing Xiaotangshan Hospital, Beijing, China
  • Dawei Wang Institute of Advanced Research, Infervision Medical Technology Co., Ltd., Beijing, China
  • Ying Cao Institute of Advanced Research, Infervision Medical Technology Co., Ltd., Beijing, China
  • Zhenhao Zhao Institute of Advanced Research, Infervision Medical Technology Co., Ltd., Beijing, China
  • Yingmin Ma Department of Respiratory and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China

DOI:

https://doi.org/10.3855/jidc.15022

Keywords:

Deep learning, diagnostic systems, performance evaluation, COVID-19, asymptomatic cases

Abstract

Introduction: Our study aimed to investigate the performance of deep learning (DL)-based diagnostic systems in alerting against COVID-19, especially among asymptomatic individuals coming from overseas, and to analyze the features of identified asymptomatic patients in detail.

Methodology: DL diagnostic systems were deployed to assist in the screening of COVID-19, including the pneumonia system and pulmonary nodules system. 1,917 overseas returnees who underwent CT examination and rRT-PCR tests were enrolled. DL pneumonia system promptly alerted clinicians to suspected COVID-19 after CT examinations while the performance was evaluated with rRT-PCR results as the reference. The radiological features of asymptomatic COVID-19 cases were described according to the Nomenclature of the Fleischner Society. 

Results: Fifty-three cases were confirmed as COVID-19 patients by rRT-PCR tests, including 5 asymptomatic cases. DL pneumonia system correctly alerted 50 cases as suspected COVID-19 with a sensitivity of 0.9434 and specificity of 0.9592 (within 2 minutes per case); while the pulmonary nodules system alerted 2 of the 3 missed asymptomatic cases. Additionally, five asymptomatic patients presented different characteristics such as elevated creatine kinase level and prolonged prothrombin time, as well as atypical radiological features.

Conclusions: DL diagnostic systems are promising complementary approaches for prompt screening of imported COVID-19 patients, even the imported asymptomatic cases. Unique clinical and radiological characteristics of asymptomatic cases might be of great value in screening as well. 

Advances in knowledge: DL-based systems are practical, efficient, and reliable to assist radiologists in screening COVID-19 patients. Differential features of asymptomatic patients might be useful to clinicians in the frontline to differentiate asymptomatic cases.

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Published

2022-11-29

How to Cite

1.
Dong D, Luo Z, Zheng Y, Liang Y, Zhao P, Feng L, Wang D, Cao Y, Zhao Z, Ma Y (2022) Application of deep learning-based diagnostic systems in screening asymptomatic COVID-19 patients among oversea returnees. J Infect Dev Ctries 16:1706–1714. doi: 10.3855/jidc.15022

Issue

Section

Coronavirus Pandemic