Identifying and Visualizing Global Research Trends and Hotspots of Artificial Intelligence in Medical Ultrasound: A Bibliometric Analysis.

IF 1.1 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Current Medical Imaging Reviews Pub Date : 2024-01-01 DOI:10.2174/0115734056324388240919112351
Jinting Xiao, Fajuan Shen, Weizhao Lu, Zaiyang Yu, Shengjie Li, Jianlin Wu
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引用次数: 0

Abstract

Background: Applications of artificial intelligence (AI) in medical ultrasound have rapidly grown in recent years. Therefore, it is necessary to identify and visualize global research trends and hotspots of AI in medical ultrasound to provide guidance for further exploitation.

Objective: This study aims to highlight the global research trends and hotspots of the top 100 most-cited papers related to AI in medical ultrasound by combining quantitative and visualization methods.

Methods: Articles on AI in medical ultrasound were selected from the WoSCC database and ranked by citation count. After identifying the 100 most-cited papers, we conducted a quantitative and visualized analysis of bibliometric characteristics, including leading research countries, prominent institutions, key authors and journals, author clusters and collaborations, and keyword co-occurrence network analysis.

Results: The top 100 highly cited papers from the WoSCC database were published between 1999 and 2021, with total citations ranging from 91 to 1580. The most cited article was published in IEEE Transactions on Medical Imaging. The top three most prolific countries/regions were the United States, mainland China, and the United Kingdom. The most published institutions and journals were Idaho University and IEEE Transactions on Medical Imaging. Twelve authors published more than four papers, with Suri, JS being the most productive author. The most studied topics were "ultrasound", "computer-aided diagnosis", and "segmentation". Ultrasonography of Superficial Organs was the main site that was studied the most.

Conclusion: This study provides comprehensive insights into the characteristics of AI in medical ultrasound through quantitative and visualized analysis of the most highly cited literature. It serves as a valuable reference for the development and applications of AI, fostering potential collaborations within this domain.

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识别和可视化医学超声领域人工智能的全球研究趋势和热点:文献计量分析
背景:近年来,人工智能(AI)在医学超声领域的应用迅速发展。因此,有必要对人工智能在医学超声领域的全球研究趋势和热点进行识别和可视化,为进一步开发提供指导:本研究旨在通过定量与可视化相结合的方法,突出医学超声领域人工智能相关论文被引用次数最多的前 100 篇论文的全球研究趋势和热点:从 WoSCC 数据库中选取有关医学超声领域人工智能的文章,并按引用次数进行排序。在确定 100 篇高被引论文后,我们对文献计量学特征进行了定量和可视化分析,包括主要研究国家、著名机构、关键作者和期刊、作者集群和合作以及关键词共现网络分析:WoSCC 数据库中的前 100 篇高被引论文发表于 1999 年至 2021 年之间,总被引次数从 91 次到 1580 次不等。被引用次数最多的文章发表在《IEEE 医学影像论文集》(IEEE Transactions on Medical Imaging)上。论文最多的前三个国家/地区分别是美国、中国大陆和英国。发表文章最多的机构和期刊是爱达荷大学和《IEEE 医学影像论文集》。有 12 位作者发表了 4 篇以上的论文,其中苏里(Suri, JS)是发表论文最多的作者。研究最多的主题是 "超声"、"计算机辅助诊断 "和 "分割"。结论:本研究通过对引用率最高的文献进行定量和可视化分析,全面揭示了医学超声领域人工智能的特点。它为人工智能的发展和应用提供了有价值的参考,促进了这一领域的潜在合作。
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来源期刊
CiteScore
2.60
自引率
0.00%
发文量
246
审稿时长
1 months
期刊介绍: Current Medical Imaging Reviews publishes frontier review articles, original research articles, drug clinical trial studies and guest edited thematic issues on all the latest advances on medical imaging dedicated to clinical research. All relevant areas are covered by the journal, including advances in the diagnosis, instrumentation and therapeutic applications related to all modern medical imaging techniques. The journal is essential reading for all clinicians and researchers involved in medical imaging and diagnosis.
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