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• Highlights •
Highlights of the 107th scientific assembly and annual meeting of Radiological Society of North America: Artificial intelligence
LIN Jieqiong  HUANG Yanqi  LIANG Changhong  ZENG Hongwu 

Cite this article as: Lin JQ, Huang YQ, Liang CH, et al. Highlights of the 107th scientific assembly and annual meeting of Radiological Society of North America: Artificial intelligence[J]. Chin J Magn Reson Imaging, 2022, 13(3): 111-114, 121. DOI:10.12015/issn.1674-8034.2022.03.027.


[Abstract] The highlights of artificial intelligence (AI) at the 107th scientific assembly and annual meeting of Radiological Society of North America (RSNA) were: (1) Advanced technologies and algorithms: federated learning aimed to solve the problem of data island. Transfer learning has been applied to multicenter studies; (2) As the new concept of real age, 'Image-based physiological Age' was first time raised; (3) AI empowers imaging, from laboratory to clinical applications, including early diagnosis, risk assessment, prognostic prediction, clinical decision support and automatic intelligent measurement, etc; (4) Application of AI also meets challenges such as data 'black box', model applicability, data management and legal liability. AI related studies published in recent years and 2021 RSNA were reviewed in this article.
[Keywords] artificial intelligence；deep learning；convolutional neural network；federated learning；transfer learning；radiomics

LIN Jieqiong1, 2   HUANG Yanqi3   LIANG Changhong3   ZENG Hongwu1  

1 Department of Radiology, Shenzhen Children's Hospital, Shenzhen 518038, China

2 Shantou University Medical College, Shantou 515041, China

3 Department of Radiology, Guangdong General Hospital, Guangzhou 510080, China

Liang CH, E-mail: liangchanghong@gdph.org.cn Zeng HW, E-mail: homerzeng@126.com

Conflicts of interest   None.

ACKNOWLEDGMENTS Youth Project of National Natural Science Foundation of China (No. 81701782); Sanming Project of Medicine in Shenzhen (No. SZSM202011005).
Received  2021-12-23
Accepted  2022-02-21
DOI: 10.12015/issn.1674-8034.2022.03.027
Cite this article as: Lin JQ, Huang YQ, Liang CH, et al. Highlights of the 107th scientific assembly and annual meeting of Radiological Society of North America: Artificial intelligence[J]. Chin J Magn Reson Imaging, 2022, 13(3): 111-114, 121. DOI:10.12015/issn.1674-8034.2022.03.027.

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