Abstract：
The field of neurosurgery has consistently represented an area of innovation and integration of technology since its inception. As such, machine learning (ML) has found its way into applications within neurosurgery relatively rapidly. Through this bibliometric review and cluster analysis, we seek to identify trends and emerging applications of ML within neurosurgery.
A bibliometric analysis was carried out in the Web of Science database on publications from January 2000 to March 2023. The full data set of the 200 most cited publications including title, author information, journal, citation count, keywords, and abstracts for each publication was evaluated in CiteSpace. CiteSpace was used to elucidate publication characteristics, trends, and topic clusters via collaborate network analysis using the Kamada-Kawai algorithm.
The 25 most cited titles were included in our analysis. Harvard University and its affiliates represented the top institution, contributing nearly 25% of publications in the literature. WORLD NEUROSURGERY was the journal with the highest net citation count of 747 (29%). Collaborative network analysis generated 12 unique clusters, the largest of which was machine learning, followed by feature importance and deep brain stimulation.
This review highlights the most impactful articles pertaining to ML in the field of neurosurgery. ML has been applied into several sub-specialties within neurosurgery to optimize patient care, with special attention to outcome predictors, patient selection, and surgical decision making.
A bibliometric analysis was carried out in the Web of Science database on publications from January 2000 to March 2023. The full data set of the 200 most cited publications including title, author information, journal, citation count, keywords, and abstracts for each publication was evaluated in CiteSpace. CiteSpace was used to elucidate publication characteristics, trends, and topic clusters via collaborate network analysis using the Kamada-Kawai algorithm.
The 25 most cited titles were included in our analysis. Harvard University and its affiliates represented the top institution, contributing nearly 25% of publications in the literature. WORLD NEUROSURGERY was the journal with the highest net citation count of 747 (29%). Collaborative network analysis generated 12 unique clusters, the largest of which was machine learning, followed by feature importance and deep brain stimulation.
This review highlights the most impactful articles pertaining to ML in the field of neurosurgery. ML has been applied into several sub-specialties within neurosurgery to optimize patient care, with special attention to outcome predictors, patient selection, and surgical decision making.
摘要：
背景：神经外科领域自成立以来一直代表着创新和技术集成的领域。因此，机器学习(ML)已经相对较快地应用于神经外科。通过文献计量回顾和聚类分析，我们寻求确定ML在神经外科中的趋势和新兴应用。
方法：于2000年1月至2023年3月在WebofScience数据库中进行了文献计量分析。200种引用最多的出版物的完整数据集，包括标题，作者信息,journal,引用计数,关键词,每个出版物的摘要都在CiteSpace中进行了评估。CiteSpace被用来阐明出版物的特点，趋势,并使用Kamada-Kawai算法通过协作网络分析进行主题聚类。
结果：我们的分析中包含了25个引用最多的标题。哈佛大学及其附属机构代表了顶级机构，贡献了近25%的文献出版物。世界神经外科杂志是最高的净引用数747（29％）。协作网络分析生成了12个独特的集群，其中最大的是机器学习，其次是特征重要性，和深部脑刺激。
结论：这篇综述重点介绍了神经外科领域中与ML有关的最有影响力的文章。ML已应用于神经外科的几个子专业，以优化患者护理，特别关注结果预测因子，患者选择,和手术决策。
方法：于2000年1月至2023年3月在WebofScience数据库中进行了文献计量分析。200种引用最多的出版物的完整数据集，包括标题，作者信息,journal,引用计数,关键词,每个出版物的摘要都在CiteSpace中进行了评估。CiteSpace被用来阐明出版物的特点，趋势,并使用Kamada-Kawai算法通过协作网络分析进行主题聚类。
结果：我们的分析中包含了25个引用最多的标题。哈佛大学及其附属机构代表了顶级机构，贡献了近25%的文献出版物。世界神经外科杂志是最高的净引用数747（29％）。协作网络分析生成了12个独特的集群，其中最大的是机器学习，其次是特征重要性，和深部脑刺激。
结论：这篇综述重点介绍了神经外科领域中与ML有关的最有影响力的文章。ML已应用于神经外科的几个子专业，以优化患者护理，特别关注结果预测因子，患者选择,和手术决策。
