Robotic-Assisted Knee Arthroplasty: An Overview. Review uri icon

Overview

abstract

  • Unicompartmental knee arthroplasty and total knee arthroplasty are reliable treatment options for osteoarthritis. In order to improve survivorship rates, variables that are intraoperatively controlled by the orthopedic surgeon are being evaluated. These variables include lower leg alignment, soft tissue balance, joint line maintenance, and tibial and femoral component alignment, size, and fixation methods. Since tighter control of these factors is associated with improved outcomes of knee arthroplasty, several computer-assisted surgery systems have been developed. These systems differ in the number and type of variables they control. Robotic-assisted systems control these aforementioned variables and, in addition, aim to improve the surgical precision of the procedure. Robotic-assisted systems are active, semi-active, or passive, depending on how independently the systems perform maneuvers. Reviewing the robotic-assisted knee arthroplasty systems, it becomes clear that these systems can accurately and reliably control the aforementioned variables. Moreover, these systems are more accurate and reliable in controlling these variables when compared to the current gold standard of conventional manual surgery. At present, few studies have assessed the survivorship and functional outcomes of robotic-assisted surgery, and no sufficiently powered studies were identified that compared survivorship or functional outcomes between robotic-assisted and conventional knee arthroplasty. Although preliminary outcomes of robotic-assisted surgery look promising, more studies are necessary to assess if the increased accuracy and reliability in controlling the surgical variables leads to better outcomes of robotic-assisted knee arthroplasty.

publication date

  • January 1, 2016

Research

keywords

  • Arthroplasty, Replacement, Knee
  • Knee Joint
  • Osteoarthritis, Knee
  • Robotic Surgical Procedures

Identity

Scopus Document Identifier

  • 84992757805

PubMed ID

  • 27327911

Additional Document Info

volume

  • 45

issue

  • 4