Suppr超能文献

人工智能与肾脏病:系统评价与文献计量分析,PRISMA-ScR。

Artificial intelligence with kidney disease: A scoping review with bibliometric analysis, PRISMA-ScR.

机构信息

Department of Internal Medicine.

Department of Neurology, Inje University Haeundae Paik Hospital, Busan, Korea.

出版信息

Medicine (Baltimore). 2021 Apr 9;100(14):e25422. doi: 10.1097/MD.0000000000025422.

Abstract

BACKGROUND

Artificial intelligence (AI) has had a significant impact on our lives and plays many roles in various fields. By analyzing the past 30 years of AI trends in the field of nephrology, using a bibliography, we wanted to know the areas of interest and future direction of AI in research related to the kidney.

METHODS

Using the Institute for Scientific Information Web of Knowledge database, we searched for articles published from 1990 to 2019 in January 2020 using the keywords AI; deep learning; machine learning; and kidney (or renal). The selected articles were reviewed manually at the points of citation analysis.

RESULTS

From 218 related articles, we selected the top fifty with 1188 citations in total. The most-cited article was cited 84 times and the least-cited one was cited 12 times. These articles were published in 40 journals. Expert Systems with Applications (three articles) and Kidney International (three articles) were the most cited journals. Forty articles were published in the 2010s, and seven articles were published in the 2000s. The top-fifty most cited articles originated from 17 countries; the USA contributed 16 articles, followed by Turkey with four articles. The main topics in the top fifty consisted of tumors (11), acute kidney injury (10), dialysis-related (5), kidney-transplant related (4), nephrotoxicity (4), glomerular disease (4), chronic kidney disease (3), polycystic kidney disease (2), kidney stone (2), kidney image (2), renal pathology (2), and glomerular filtration rate measure (1).

CONCLUSIONS

After 2010, the interest in AI and its achievements increased enormously. To date, AIs have been investigated using data that are relatively easy to access, for example, radiologic images and laboratory results in the fields of tumor and acute kidney injury. In the near future, a deeper and wider range of information, such as genetic and personalized database, will help enrich nephrology fields with AI technology.

摘要

背景

人工智能(AI)对我们的生活产生了重大影响,并在各个领域发挥着重要作用。通过分析过去 30 年来肾脏病学领域的 AI 趋势,使用文献库,我们想了解 AI 在与肾脏相关的研究中的兴趣领域和未来方向。

方法

我们使用 Institute for Scientific Information Web of Knowledge 数据库,于 2020 年 1 月使用 AI、深度学习、机器学习和肾脏(或肾脏)等关键词搜索了 1990 年至 2019 年发表的文章。选择的文章在引文分析点进行手动审查。

结果

从 218 篇相关文章中,我们选择了总引用次数为 1188 次的前 50 篇文章。被引次数最多的文章被引 84 次,被引次数最少的文章被引 12 次。这些文章发表在 40 种期刊上。《专家系统与应用》(三篇文章)和《国际肾脏病》(三篇文章)是被引次数最多的期刊。40 篇文章发表于 21 世纪 10 年代,7 篇文章发表于 2000 年代。前 50 篇最受引用的文章来自 17 个国家;美国贡献了 16 篇文章,土耳其以 4 篇紧随其后。前 50 篇最受引用的文章主要涉及肿瘤(11)、急性肾损伤(10)、透析相关(5)、肾移植相关(4)、肾毒性(4)、肾小球疾病(4)、慢性肾病(3)、多囊肾病(2)、肾结石(2)、肾脏图像(2)、肾脏病理学(2)和肾小球滤过率测量(1)。

结论

2010 年后,人们对 AI 及其成果的兴趣大大增加。迄今为止,AI 已经在肿瘤和急性肾损伤等领域使用相对容易获取的数据进行了研究。在不久的将来,更深入、更广泛的信息,如遗传和个性化数据库,将有助于将 AI 技术应用于肾脏病学领域。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/55e3/8036048/453a982d00ce/medi-100-e25422-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验