超声波乳腺癌检测:对 2004 年至 2024 年全球趋势的文献计量分析。

Ya-Yu Sun, Xiao-Tong Shi, Li-Long Xu
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引用次数: 0

摘要

随着计算机技术和成像设备的发展,超声波已成为乳腺癌诊断的重要工具。为了更深入地了解超声诊断乳腺癌的研究现状,本研究采用文献计量学方法进行了全面分析,分析时间跨度从 2004 年到 2024 年,分析了来自 82 个国家/地区 2176 个机构的 3523 篇文章。在此期间,从 2004 年到 2024 年,有关乳腺癌超声诊断的论文呈波动增长趋势。值得注意的是,中国、首尔国立大学和 Kim EK 成为乳腺癌超声检测领域的主要贡献者,发表和引用最多的期刊是《超声医学生物学》和《放射学》。该领域的研究热点包括 "乳腺病变"、"致密乳腺 "和 "保乳手术",而 "机器学习"、"超声成像"、"卷积神经网络"、"病例报告"、"病理完全反应"、"深度学习"、"人工智能 "和 "分类 "预计将成为未来的研究前沿。这种开创性的超声乳腺癌诊断出版物文献计量分析和可视化为临床医学专业人员提供了可靠的研究重点和方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Ultrasound for breast cancer detection: A bibliometric analysis of global trends between 2004 and 2024.

With the advancement of computer technology and imaging equipment, ultrasound has emerged as a crucial tool in breast cancer diagnosis. To gain deeper insights into the research landscape of ultrasound in breast cancer diagnosis, this study employed bibliometric methods for a comprehensive analysis spanning from 2004 to 2024, analyzing 3523 articles from 2176 institutions in 82 countries/regions. Over this period, publications on ultrasound diagnosis of breast cancer showed a fluctuating growth trend from 2004 to 2024. Notably, China, Seoul National University and Kim EK emerged as leading contributors in ultrasound for breast cancer detection, with the most published and cited journals being Ultrasound Med Biol and Radiology. The research spots in this area included "breast lesion", "dense breast" and "breast-conserving surgery", while "machine learning", "ultrasonic imaging", "convolutional neural network", "case report", "pathological complete response", "deep learning", "artificial intelligence" and "classification" are anticipated to become future research frontiers. This groundbreaking bibliometric analysis and visualization of ultrasonic breast cancer diagnosis publications offer clinical medical professionals a reliable research focus and direction.

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