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Tracing the evolution of robotic-assisted total knee arthroplasty: a bibliometric analysis of the top 100 highly cited articles

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Abstract

Robotic-assisted surgical systems hold promise in enhancing total knee arthroplasty (TKA) outcomes and patients’ quality of life. This study aims to comprehensively analyze the literature on robot-assisted total knee arthroplasty (r-TKA), providing insights into its current development, clinical application, and research trends. A systematic search was conducted in the Web of Science Core Collection (WOSCC) to identify relevant articles. Data were collected from the top 100 highly cited articles. Article evidence levels were assessed following established guidelines. Statistical analyses and visualizations were performed to reveal publication trends, citations, research hotspots, and collaborative networks. The analysis covered 100 highly cited articles meeting the research criteria, with a focus on the last five years. The United States emerged as a major contributor, with most publications and citations in the Journal of Knee Surgery and Knee Surgery Sports Traumatology Arthroscopy. Research priorities revolved around clinical outcomes, accuracy, and alignment of r-TKA. Notably, higher evidence levels correlated with more citations, indicating greater attention. Interest in and research on r-TKA is steadily increasing, with a few countries at the forefront of these endeavors. While numerous studies have already reported short- to medium-term follow-up results, it is crucial to conduct longer-term investigations to gain a more comprehensive understanding of the clinical benefits that r-TKA offers compared to conventional techniques. Through ongoing research and a greater embrace of robotic technology, we can continue to improve the quality of life for patients undergoing knee arthroplasty.

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Data Availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

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Funding

This work was supported by the National Natural Science Foundation of China (Grant No. 82202672), the Key Research and Development Program of Anhui Province (No. 2022e07020017), China Postdoctoral Science Foundation Grant (2022M723049), the Natural Science Foundation of Anhui Province(2108085QH319), Anhui Provincial Research Preparation Plan (2022AH040074), the Joint Fund for Medical Artificial Intelligence (MAI2022C002) and the Fundamental Research Funds for the Central Universities (WK9110000173).

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Author notes

  1. Yang Yao and Wang Yingjie have contributed equally to this manuscript.

Authors and Affiliations

  1. Department of Orthopedics, The Second Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui Province, China

    Yao Yang, Yingjie Wang, Yuan Chen, Junjie Wang & Junchen Zhu

  2. Division of Life Sciences and Medicine, Department of Orthopedics, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, China

    Yao Yang, Yingjie Wang, Yuan Chen, Junjie Wang, Baoliang Lu, Chen Zhu & Xianzuo Zhang

  3. Graduate School of Bengbu Medical College, Bengbu, China

    Baoliang Lu

  4. Department of Orthopedics, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai Jiao Tong University, Shanghai, China

    Wanbo Zhu

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Contributions

ZX, ZC, ZJ and ZW conceived the idea for this study. YY, WY, CY, and WJ carried out the data extraction. The statistical analysis was performed by YY. Image plotting was conducted by YY, LB, WY and ZW. YY, WY and ZX drafted the initial manuscript, while all co-authors provided critical feedback and revisions to the final version. ZX and ZJ provided financial support, and ZC and ZJ provided administrative assistance. All authors reviewed the manuscript.

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Correspondence to Wanbo Zhu, Junchen Zhu, Chen Zhu or Xianzuo Zhang.

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Yang, Y., Wang, Y., Chen, Y. et al. Tracing the evolution of robotic-assisted total knee arthroplasty: a bibliometric analysis of the top 100 highly cited articles. J Robotic Surg 17, 2973–2985 (2023). https://doi.org/10.1007/s11701-023-01742-4

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