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 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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