PDF(Pubmed)
Abstract：
UNASSIGNED: Artificial intelligence has demonstrated utility in orthopedic research. Algorithmic models derived from machine learning have demonstrated adaptive learning with predictive application towards outcomes, leading to increased traction in the literature. This study aims to identify machine learning arthroplasty research trends and anticipate emerging key terms.
UNASSIGNED: Published literature focused on machine learning in arthroplasty from 1992 to 2023 was selected through the Web of Science Core Collection of Clarivate Analytics. Following that, bibliometric indicators were attained and brought in to perform an additional examination using Bibliometrix and VOSviewer to identify historical and present patterns within the literature.
UNASSIGNED: A total of 235 documents were obtained through bibliometric sourcing based on machine learning applications within the arthroplasty literature. Thirty-four countries published articles on the topic, and the United States was demonstrated to be the largest global contributor. Four hundred-five institutions internationally contributed articles, with Harvard Medical School and the University of California system as the most relevant institutes, with 75 and 44 articles produced, respectively. Kwon YM was the most productive author, while Haeberle HS and Ramkumar PN were the most impactful based on h-index. The Thematic map and Co-occurrence visualization helped identify both major and niche themes present in the scientific databases.
UNASSIGNED: Machine learning in arthroplasty research continues to gain traction with a growing annual production rate and contributions from international authors and institutions. Institutions and authors based in the United States are the leading contributors to machine learning applications within arthroplasty research. This research discerns trends that have occurred, are presently ongoing, and are emerging within this field, aiming to inform future hotspot development.
UNASSIGNED: Published literature focused on machine learning in arthroplasty from 1992 to 2023 was selected through the Web of Science Core Collection of Clarivate Analytics. Following that, bibliometric indicators were attained and brought in to perform an additional examination using Bibliometrix and VOSviewer to identify historical and present patterns within the literature.
UNASSIGNED: A total of 235 documents were obtained through bibliometric sourcing based on machine learning applications within the arthroplasty literature. Thirty-four countries published articles on the topic, and the United States was demonstrated to be the largest global contributor. Four hundred-five institutions internationally contributed articles, with Harvard Medical School and the University of California system as the most relevant institutes, with 75 and 44 articles produced, respectively. Kwon YM was the most productive author, while Haeberle HS and Ramkumar PN were the most impactful based on h-index. The Thematic map and Co-occurrence visualization helped identify both major and niche themes present in the scientific databases.
UNASSIGNED: Machine learning in arthroplasty research continues to gain traction with a growing annual production rate and contributions from international authors and institutions. Institutions and authors based in the United States are the leading contributors to machine learning applications within arthroplasty research. This research discerns trends that have occurred, are presently ongoing, and are emerging within this field, aiming to inform future hotspot development.
摘要：
■人工智能已在骨科研究中显示出实用性。从机器学习中得出的算法模型已经证明了自适应学习具有对结果的预测性应用，导致文献中的牵引力增加。这项研究旨在确定机器学习关节成形术的研究趋势，并预测新出现的关键术语。
■通过ClarivateAnalytics的WebofScience核心合集选择了1992年至2023年专注于关节成形术中机器学习的已发表文献。在此之后,获得了文献计量指标，并使用Bibliometrix和VOSviewer进行了额外的检查，以确定文献中的历史和当前模式。
■通过基于关节成形术文献中的机器学习应用的文献计量来源获得了总共235篇文献。34个国家发表了有关该主题的文章，美国被证明是最大的全球贡献者。四百五家机构在国际上投稿，哈佛医学院和加州大学系统是最相关的机构，生产了75和44篇文章，分别。KwonYM是最有成效的作者,而根据h指数，HaeberleHS和RamkumarPN的影响最大。专题图和共现可视化有助于确定科学数据库中存在的主要和利基主题。
■关节成形术研究中的机器学习以不断增长的年生产率和国际作者和机构的贡献继续获得牵引力。位于美国的机构和作者是关节成形术研究中机器学习应用的主要贡献者。这项研究发现了已经发生的趋势，目前正在进行中，并在这个领域出现，旨在为未来热点发展提供信息。
■通过ClarivateAnalytics的WebofScience核心合集选择了1992年至2023年专注于关节成形术中机器学习的已发表文献。在此之后,获得了文献计量指标，并使用Bibliometrix和VOSviewer进行了额外的检查，以确定文献中的历史和当前模式。
■通过基于关节成形术文献中的机器学习应用的文献计量来源获得了总共235篇文献。34个国家发表了有关该主题的文章，美国被证明是最大的全球贡献者。四百五家机构在国际上投稿，哈佛医学院和加州大学系统是最相关的机构，生产了75和44篇文章，分别。KwonYM是最有成效的作者,而根据h指数，HaeberleHS和RamkumarPN的影响最大。专题图和共现可视化有助于确定科学数据库中存在的主要和利基主题。
■关节成形术研究中的机器学习以不断增长的年生产率和国际作者和机构的贡献继续获得牵引力。位于美国的机构和作者是关节成形术研究中机器学习应用的主要贡献者。这项研究发现了已经发生的趋势，目前正在进行中，并在这个领域出现，旨在为未来热点发展提供信息。
