A bibliometric and visual analysis of artificial intelligence technologies-enhanced brain MRI research
Article
Article Title | A bibliometric and visual analysis of artificial |
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ERA Journal ID | 18083 |
Article Category | Article |
Authors | Chen, Xieling (Author), Zhang, Xinxin (Author), Xie, Haoran (Author), Tao, Xiaohui (Author), Wang, Fu Lee (Author), Xie, Nengfu (Author) and Hao, Tianyong (Author) |
Journal Title | Multimedia Tools and Applications |
Journal Citation | 80, pp. 17335-17363 |
Number of Pages | 29 |
Year | 2021 |
Publisher | Springer |
Place of Publication | United States |
ISSN | 1380-7501 |
1573-7721 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s11042-020-09062-7 |
Web Address (URL) | https://link.springer.com/article/10.1007/s11042-020-09062-7 |
Abstract | With the advances and development of imaging and computer technologies, the application of artificial intelligence (AI) in the processing of magnetic resonance imaging (MRI) data has become a significant research field. Based on 2572 research articles concerning AI-enhanced brain MRI processing, this study provides a latent Dirichlet allocation based bibliometric analysis for the exploration of the status, trends, major research issues, and potential future directions of the research field. The trend analyses of articles and citations demonstrate a flourishing and increasing impact of the research. Neuroimage is the most prolific and influential journal. The USA and University College London have contributed the most to the research. The collaboration between European countries is very close. Essential research issues such as Image segmentation, Mental disorder, Functional network connectivity, and Alzheimer’s disease have been uncovered. Potential inter-topic research directions such as Functional network connectivity and Mental disorder, Image segmentation and Image classification, Cognitive impairment and Diffusion imaging, as well as Sense and memory and Emotion and feedback, have been highlighted. |
Keywords | artificial intelligence, Magnetic resonance imaging, Latent Dirichlet allocation |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
420313. Mental health services | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Education University of Hong Kong, China |
South China Normal University, China | |
Lingnan University of Hong Kong, China | |
School of Sciences | |
Hong Kong Metropolitan University, China | |
Chinese Academy of Agricultural Sciences, China | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q5wzv/a-bibliometric-and-visual-analysis-of-artificial-intelligence-technologies-enhanced-brain-mri-research
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