智能超声成像诊断甲状腺结节的文献计量学分析。

Q4 Medicine
Yang Li, Jian-Lin Wang, Jiao-Jiao Ma, Zhe Sun, Bo Zhang
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引用次数: 0

摘要

目的通过文献计量学方法探讨智能超声成像在甲状腺结节诊断中的研究进展及热点,明确研究方向。方法检索Web of Science Core Collection中2004年1月至2024年8月期间关于智能超声成像诊断甲状腺结节的相关研究文章。Python用于分析年度出版物的数量。使用VOSviewer创建作者共现网络和关键词密度图。使用CiteSpace演示了期刊的双地图叠加,以及共引和关键词的爆发和聚类。结果共纳入文献1 179篇。年度出版物数量稳步增长。所涉期刊质量较高,出版物呈现交叉研究趋势。中国研究人员是该领域的核心研究力量。Haugen等人关于甲状腺结节指南的研究被引用最多。共被引和关键词的聚类表明研究涉及多个领域。甲状腺结节、癌症、深度学习是该领域的代表性关键词。结论研究课题的不断丰富,促进了甲状腺结节智能超声成像的快速发展。基于深度学习的智能诊断方法可以提供诊断建议,但仍存在解释等挑战。智能诊断算法与医学知识的深度结合是研究方向之一。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bibliometric Analysis of Intelligent Ultrasound Imaging in the Diagnosis of Thyroid Nodules.

Objective To explore the research progress and hotspots of intelligent ultrasound imaging in the diagnosis of thyroid nodules and clarify the research directions via the bibliometric method.Methods The relevant research articles on intelligent ultrasound imaging in the diagnosis of thyroid nodules were retrieved from the Web of Science Core Collection,covering the period from January 2004 to August 2024.Python was used to analyze the number of annual publications.VOSviewer was used to create the co-occurrence network of authors and the keyword density map.CiteSpace was used to demonstrate the dual-map overlays of the journals,as well as the bursts and clustering of co-citations and keywords.Results A total of 1 179 articles were included.The annual number of publications increased steadily.The involved journals demonstrated high quality,and the publications showed a trend of cross-research.Chinese researchers were the core research force in this field.Haugen et al.'s study on the guidelines for thyroid nodules had the most citations.The clustering of co-citations and keywords indicated studies in multiple fields.Thyroid nodules,cancer,and deep learning were the representative keywords in this field.Conclusions The continuous enrichment of research topics promotes the rapid development of intelligent ultrasound imaging for thyroid nodules.Intelligent diagnosis methods based on deep learning can provide diagnostic suggestions,while there are still challenges such as interpretation.One of the research directions is the deep combination of intelligent diagnosis algorithms and medical knowledge.

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来源期刊
中国医学科学院学报
中国医学科学院学报 Medicine-Medicine (all)
CiteScore
0.60
自引率
0.00%
发文量
6813
期刊介绍: Acta Academiae Medicinae Sinicae was founded in February 1979. It is a comprehensive medical academic journal published in China and abroad, supervised by the Ministry of Health of the People's Republic of China and sponsored by the Chinese Academy of Medical Sciences and Peking Union Medical College. The journal mainly reports the latest research results, work progress and dynamics in the fields of basic medicine, clinical medicine, pharmacy, preventive medicine, biomedicine, medical teaching and research, aiming to promote the exchange of medical information and improve the academic level of medicine. At present, the journal has been included in 10 famous foreign retrieval systems and their databases [Medline (PubMed online version), Elsevier, EMBASE, CA, WPRIM, ExtraMED, IC, JST, UPD and EBSCO-ASP]; and has been included in important domestic retrieval systems and databases [China Science Citation Database (Documentation and Information Center of the Chinese Academy of Sciences), China Core Journals Overview (Peking University Library), China Science and Technology Paper Statistical Source Database (China Science and Technology Core Journals) (China Institute of Scientific and Technological Information), China Science and Technology Journal Paper and Citation Database (China Institute of Scientific and Technological Information)].
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