A bibliometric and visual analysis of artificial intelligence technologies-enhanced brain MRI research

Chen, Xieling and Zhang, Xinxin and Xie, Haoran and Tao, Xiaohui ORCID: https://orcid.org/0000-0002-0020-077X and Wang, Fu Lee and Xie, Nengfu and Hao, Tianyong (2020) A bibliometric and visual analysis of artificial intelligence technologies-enhanced brain MRI research. Multimedia Tools and Applications. ISSN 1380-7501


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.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Files associated with this item cannot be displayed due to copyright restrictions.
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 -)
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 -)
Date Deposited: 27 Aug 2020 05:22
Last Modified: 31 Aug 2020 04:42
Uncontrolled Keywords: artificial intelligence, Magnetic resonance imaging, Latent Dirichlet allocation
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080199 Artificial Intelligence and Image Processing not elsewhere classified
11 Medical and Health Sciences > 1117 Public Health and Health Services > 111714 Mental Health
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: https://doi.org/10.1007/s11042-020-09062-7
URI: http://eprints.usq.edu.au/id/eprint/39301

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