A literature review of robotic systems showed that these systems can , similarly to computer navigation , reliably improve lower leg alignment , component positioning and soft tissues balancing .
Findings in this study suggest that computer navigation or robotic assistance may help managing these multiple variables and could improve outcomes .
Recently , there is a growing interest in surgical variables that are intraoperatively controlled by orthopaedic surgeons , including lower leg alignment , component positioning and soft tissues balancing .
Since more tight control over these factors is associated with improved outcomes of unicompartmental knee arthroplasty and total knee arthroplasty ( TKA ) , several computer navigation and robotic-assisted systems have been developed .
Although mechanical axis accuracy and component positioning have been shown to improve with computer navigation , no superiority in functional outcomes has yet been shown .
This could be explained by the fact that many differences exist between the number and type of surgical variables these systems control .
Most systems control lower leg alignment and component positioning , while some in addition control soft tissue balancing .
Finally , robotic-assisted systems have the additional advantage of improving surgical precision .
Manually controlling all these surgical variables can be difficult for the orthopaedic surgeon .
Sequential versus automated cutting guides in computer-assisted total knee arthroplasty.
The accuracy and efficiency of automated cutting guides in CAS systems have not been previously compared with conventional CAS techniques . Therefore , it is not yet clear if these more advanced technologies are warranted . We hypothesized that a novel automated cutting guide with CAS for total knee arthroplasty would be more efficient and more accurate than conventional navigation with sequential cutting blocks . Twelve cadaver legs were used in total . Each leg was r and omly assigned to either an automated guide positioning or a conventional freeh and computer-navigated guide positioning . The guide positions postosseous fixation and the final bone-cut surfaces were digitized and compared to the targeted cutting planes . The final location of the impacted trial implant was also digitized and compared to the planned implant location . The time for each step and the total time taken to prepare the femur were measured for both groups . The mean femoral preparation time was shorter with the automated cutting guide than the conventional method ( 5.5 min versus 13.8 min , p<0.001 ) . The average deviation in the final bone resections from the planned resections was significantly lower for the automated cutting guide in the frontal/rotational plane ( 0.55 ° versus 1.1 ° ) , sagittal plane ( 0.75 ° versus 2.0 ° ) , and cut height direction ( 0.56 mm versus 1.6 mm ) . Therefore , based on these results , we concluded that automated cutting-guide positioning result ed in more efficient and more accurate femoral cuts in comparison to the conventional navigation method in a cadaveric model
A literature review of robotic systems showed that these systems can , similarly to computer navigation , reliably improve lower leg alignment , component positioning and soft tissues balancing .
Findings in this study suggest that computer navigation or robotic assistance may help managing these multiple variables and could improve outcomes .
Recently , there is a growing interest in surgical variables that are intraoperatively controlled by orthopaedic surgeons , including lower leg alignment , component positioning and soft tissues balancing .
Since more tight control over these factors is associated with improved outcomes of unicompartmental knee arthroplasty and total knee arthroplasty ( TKA ) , several computer navigation and robotic-assisted systems have been developed .
Although mechanical axis accuracy and component positioning have been shown to improve with computer navigation , no superiority in functional outcomes has yet been shown .
This could be explained by the fact that many differences exist between the number and type of surgical variables these systems control .
Most systems control lower leg alignment and component positioning , while some in addition control soft tissue balancing .
Finally , robotic-assisted systems have the additional advantage of improving surgical precision .
Manually controlling all these surgical variables can be difficult for the orthopaedic surgeon .