人工智能在自闭症谱系障碍中的研究热点与趋势的系统文献计量与可视化分析

IF 2.5 4区医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Frontiers in Neuroinformatics Pub Date : 2023-12-06 DOI:10.3389/fninf.2023.1310400

Qianfang Jia, Xiaofang Wang, Rongyi Zhou, Bingxiang Ma, Fangqin Fei, Hui Han

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引用次数: 0

摘要

人工智能(AI)一直是自闭症谱系障碍(ASD)研究的主题，并可能在未来影响其识别、诊断、干预和其他医疗实践。虽然以前的研究已经使用文献计量学技术来分析和调查人工智能，但关于人工智能在ASD中的应用的研究很少。本研究旨在探索人工智能在ASD中的广泛应用和研究前沿。方法从Web of Science Core Collection (WoSCC)数据库中检索检索数据，评估AI在ASD中的应用程度。CiteSpace.5.8。使用在线文献计量分析工具R3和VOSviewer对数据进行分析。结果共分析了291个国家和地区的776篇文献;其中，美国文献256篇，中国文献173篇，英国文献中心性最大，为0.33;斯坦福大学的h指数最高，为17;共同引用文献中最大的聚类标签是机器学习。此外，出现频率较高的关键词有自闭症谱系障碍(295)、儿童(255)、分类(156)和诊断(77)。2021 - 2023年爆发关键词为婴儿和特征选择，2022 - 2023年爆发关键词为胼胝体。本研究对人工智能在ASD中的应用文献进行了系统的分析，对该领域进行了全面的论证。在这一领域，美国和中国的出版物数量最多，英国的影响力最大，斯坦福大学的影响力最大。此外，人工智能在ASD中的应用研究多集中在分类和诊断方面，其中“婴儿”、“特征选择”、“胼胝体”处于研究前沿，为未来的研究提供了方向。然而，使用人工智能技术来识别自闭症谱系障碍需要进一步的研究。

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Systematic bibliometric and visualized analysis of research hotspots and trends in artificial intelligence in autism spectrum disorder

BackgroundArtificial intelligence (AI) has been the subject of studies in autism spectrum disorder (ASD) and may affect its identification, diagnosis, intervention, and other medical practices in the future. Although previous studies have used bibliometric techniques to analyze and investigate AI, there has been little research on the adoption of AI in ASD. This study aimed to explore the broad applications and research frontiers of AI used in ASD.MethodsCitation data were retrieved from the Web of Science Core Collection (WoSCC) database to assess the extent to which AI is used in ASD. CiteSpace.5.8. R3 and VOSviewer, two online tools for literature metrology analysis, were used to analyze the data.ResultsA total of 776 publications from 291 countries and regions were analyzed; of these, 256 publications were from the United States and 173 publications were from China, and England had the largest centrality of 0.33; Stanford University had the highest H-index of 17; and the largest cluster label of co-cited references was machine learning. In addition, keywords with a high number of occurrences in this field were autism spectrum disorder (295), children (255), classification (156) and diagnosis (77). The burst keywords from 2021 to 2023 were infants and feature selection, and from 2022 to 2023, the burst keyword was corpus callosum.ConclusionThis research provides a systematic analysis of the literature concerning AI used in ASD, presenting an overall demonstration in this field. In this area, the United States and China have the largest number of publications, England has the greatest influence, and Stanford University is the most influential. In addition, the research on AI used in ASD mostly focuses on classification and diagnosis, and “infants, feature selection, and corpus callosum are at the forefront, providing directions for future research. However, the use of AI technologies to identify ASD will require further research.

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来源期刊

Frontiers in Neuroinformatics MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES

CiteScore

4.80

自引率

5.70%

发文量

132

审稿时长

14 weeks

期刊介绍： Frontiers in Neuroinformatics publishes rigorously peer-reviewed research on the development and implementation of numerical/computational models and analytical tools used to share, integrate and analyze experimental data and advance theories of the nervous system functions. Specialty Chief Editors Jan G. Bjaalie at the University of Oslo and Sean L. Hill at the École Polytechnique Fédérale de Lausanne are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Neuroscience is being propelled into the information age as the volume of information explodes, demanding organization and synthesis. Novel synthesis approaches are opening up a new dimension for the exploration of the components of brain elements and systems and the vast number of variables that underlie their functions. Neural data is highly heterogeneous with complex inter-relations across multiple levels, driving the need for innovative organizing and synthesizing approaches from genes to cognition, and covering a range of species and disease states. Frontiers in Neuroinformatics therefore welcomes submissions on existing neuroscience databases, development of data and knowledge bases for all levels of neuroscience, applications and technologies that can facilitate data sharing (interoperability, formats, terminologies, and ontologies), and novel tools for data acquisition, analyses, visualization, and dissemination of nervous system data. Our journal welcomes submissions on new tools (software and hardware) that support brain modeling, and the merging of neuroscience databases with brain models used for simulation and visualization.