Application of artificial intelligence in chest imaging for COVID-19

被引:1
作者
Kim, Eun Young [1 ]
Chung, Myung Jin [2 ,3 ]
机构
[1] Gachon Univ, Gil Med Ctr, Dept Radiol, Coll Med, Incheon, South Korea
[2] Sungkyunkwan Univ, Samsung Med Ctr, Dept Radiol, Sch Med, Seoul, South Korea
[3] Sungkyunkwan Univ, Samsung Med Ctr, Ctr Imaging Sci, Sch Med, Seoul, South Korea
来源
JOURNAL OF THE KOREAN MEDICAL ASSOCIATION | 2021年 / 64卷 / 10期
关键词
COVID-19; Radiography; Diagnostic imaging; Artificial intelligence;
D O I
10.5124/jkma.2021.64.10.664
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Background: The coronavirus disease 2019 (COVID-19) pandemic has threatened public health. Medical imaging tools such as chest X-ray and computed tomography (CT) play an essential role in the global fight against COVID-19. Recently emerging artificial intelligence (AI) technologies further strengthen the power of imaging tools and help medical professionals. We reviewed the current progress in the development of AI technologies for the diagnostic imaging of COVID-19. Current Concepts: The rapid development of AI, including deep learning, has led to the development of technologies that may assist in the diagnosis and treatment of diseases, prediction of disease risk and prognosis, health index monitoring, and drug development. In the era of the COVID-19 pandemic, AI can improve work efficiency through accurate delineation of infections on chest X-ray and CT images, differentiation of COVID-19 from other diseases, and facilitation of subsequent disease quantification. Moreover, computer-aided platforms help radiologists make clinical decisions for disease diagnosis, tracking, and prognosis. Discussion and Conclusion: We reviewed the current progress in AI technology for chest imaging for COVID-19. However, it is necessary to combine clinical experts' observations, medical image data, and clinical and laboratory findings for reliable and efficient diagnosis and management of COVID-19. Future AI research should focus on multi-modality-based models and how to select the best model architecture for COVID-19 diagnosis and management.
引用
收藏
页码:664 / 670
页数:7
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