COVID-19 Imaging-based AI Research - A Literature Review.

Ge, Cheng; Zhang, Lili; Xie, Liangxu; Kong, Ren; Zhang, Hong; Chang, Shan

BACKGROUND The new coronavirus disease 2019 (COVID-19) is spreading rapidly around the world. Artificial intelligence (AI) assisted identification and detection of diseases is an ef-fective method of medical diagnosis. OBJECTIVES To present recent advances in AI-assisted diagnosis of COVID-19, we introduce major aspects of AI in the process of diagnosing COVID-19. METHODS In this paper, we firstly cover the latest collection and processing methods of da-tasets of COVID-19. The processing methods mainly include building public datasets, transfer learning, unsupervised learning and weakly supervised learning, semi-supervised learning methods and so on. Secondly, we introduce the algorithm application and evaluation metrics of AI in medical imaging segmentation and automatic screening. Then, we introduce the quantifi-cation and severity assessment of infection in COVID-19 patients based on image segmenta-tion and automatic screening. Finally, we analyze and point out the current AI-assisted diagno-sis of COVID-19 problems, which may provide useful clues for future work. CONCLUSION AI is critical for COVID-19 diagnosis. Combining chest imaging with AI can not only save time and effort, but also provide more accurate and efficient medical diagnosis results.