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Updated: Jan 11, 2026
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A Novel Application of Musculoskeletal Ultrasound Imaging
Published on: September 17, 2013
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启发研究动力学:医学超声波和深度学习
Hui Qin1, Yue Zhang1, Jing Zhou1
1Ultrasound Department, The Affiliated Hospital of Wuhan Sports University, Wuhan, China.
Journal of clinical ultrasound : JCU
|November 11, 2025
概括
这种图书统计分析揭示了医疗超声波中深度学习 (DL) 的快速扩展领域. 关键趋势显示了人工智能架构的进步和向外科和治疗应用的发展,强调了对标准化数据和可解释模型的需求.
科学领域:
- 医学成像和人工智能 医学成像和人工智能
- 科学文献的图书统计分析.
背景情况:
- 深度学习 (DL) 推进了医学超声波成像,但面临着诸如有限的数据集和设备可变性等挑战.
- 这项研究提供了第一个对医疗超声波中DL研究的文献统计概述.
研究的目的:
- 为医疗超声波领域的深度学习研究提供全面的文献统计概述.
- 分析出版趋势,国家/机构贡献,主题演变和合作网络.
主要方法:
- 从科学网络核心集合中获取了3386篇 (2004年至2025年4月) 关于医学超声波和DL的出版物.
- 使用文献计量工具 (Bibliometrix,VOSviewer,CiteSpace) 进行定量分析.
- 分析了出版物增长,国家/机构贡献,作者合作,场地影响和关键词主题.
主要成果:
- 年刊出版量增长了31.02%,其中中国在数量上领先,英国在引用率上领先.
- 福丹大学和作者萨巴/苏里是突出的;诊断和IEEE医疗成像交易分别在出版物和影响方面领先.
- 核心主题包括分类,细分和基于CNN的诊断,最近的重点是超声波特定诊断,先进架构 (U-Net) 和非侵入性评估.
结论:
- 医疗超声波中的DL领域正在迅速发展,并在国际上进行合作.
- 新兴趋势包括先进的人工智能架构 (U-Net,基于注意力的网络,视觉转换器) 和向外科/治疗应用的转变.
- 强调需要多中心验证,标准化数据协议和临床可解释的AI模型,以确保安全的临床翻译.
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