Application of artificial intelligence in chest imaging for COVID-19코로나19 흉부 영상에서 인공지능의 활용
- Other Titles
- 코로나19 흉부 영상에서 인공지능의 활용
- Authors
- 김은영; 정명진
- Issue Date
- Oct-2021
- Publisher
- 대한의사협회
- Keywords
- 코로나19; 방사선영상촬영; 진단영상; 인공지능; COVID-19; Radiography; Diagnostic imaging; Artificial intelligence
- Citation
- 대한의사협회지, v.64, no.10, pp.664 - 670
- Journal Title
- 대한의사협회지
- Volume
- 64
- Number
- 10
- Start Page
- 664
- End Page
- 670
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/84295
- DOI
- 10.5124/jkma.2021.64.10.664
- ISSN
- 1975-8456
- Abstract
- 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 multimodality- based models and how to select the best model architecture for COVID-19 diagnosis and management.
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