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      Robotics in Arthroplasty: A Comprehensive Review

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      The Journal of Arthroplasty
      Elsevier BV

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          Abstract

          Robotic-assisted orthopedic surgery has been available clinically in some form for over 2 decades, claiming to improve total joint arthroplasty by enhancing the surgeon's ability to reproduce alignment and therefore better restore normal kinematics. Various current systems include a robotic arm, robotic-guided cutting jigs, and robotic milling systems with a diversity of different navigation strategies using active, semiactive, or passive control systems. Semiactive systems have become dominant, providing a haptic window through which the surgeon is able to consistently prepare an arthroplasty based on preoperative planning. A review of previous designs and clinical studies demonstrate that these robotic systems decrease variability and increase precision, primarily focusing on component positioning and alignment. Some early clinical results indicate decreased revision rates and improved patient satisfaction with robotic-assisted arthroplasty. The future design objectives include precise planning and even further improved consistent intraoperative execution. Despite this cautious optimism, many still wonder whether robotics will ultimately increase cost and operative time without objectively improving outcomes. Over the long term, every industry that has seen robotic technology be introduced, ultimately has shown an increase in production capacity, improved accuracy and precision, and lower cost. A new generation of robotic systems is now being introduced into the arthroplasty arena, and early results with unicompartmental knee arthroplasty and total hip arthroplasty have demonstrated improved accuracy of placement, improved satisfaction, and reduced complications. Further studies are needed to confirm the cost effectiveness of these technologies.

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          Author and article information

          Journal
          The Journal of Arthroplasty
          The Journal of Arthroplasty
          Elsevier BV
          08835403
          October 2016
          October 2016
          : 31
          : 10
          : 2353-2363
          Article
          10.1016/j.arth.2016.05.026
          27325369
          47acea29-4778-4586-8bbe-e74cac3ca98f
          © 2016

          https://www.elsevier.com/tdm/userlicense/1.0/

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