Accuracy of a New Robotically Assisted Technique for Total Knee Arthroplasty: A Cadaveric Study

Background: Although the utility of robotic surgery has already been proven in cadaveric studies, it is our hypothesis that this newly designed robotically assisted system will achieve a high level of accuracy for bone resection. Therefore, we aimed to analyze in a cadaveric study the accuracy to achieve targeted angles and resection thickness. Methods: For this study, 15 frozen cadaveric specimens (30 knees) were used. In this study, Zimmer Biomet (Warsaw, IN) knees, navigation system, and robot (ROSA Knee System; Zimmer Biomet) were used. Eight trained, board-certified orthopedic surgeons performed robotically assisted total knee arthroplasty implantation using the same robotic protocol with 3 different implant designs. The target angles obtained from the intraoperative planning were then compared to the angles of the bone cuts performed using the robotic system and measured with the computer-assisted system considered to be the gold standard. For each bone cut the resection thickness was measured 3 times by 2 different observers and compared to the values for the planned resections. Results: All angle mean differences were below 1 degrees and standard deviations below 1 degrees. For all 6 angles, the mean differences between the target angle and the measured values were not significantly different from 0 except for the femoral flexion angle which had a mean difference of 0.95 degrees. The mean hip-knee-ankle axis difference was -0.03 degrees +/- 0.87 degrees. All resection mean differences were below 0.7 mm and standard deviations below 1.1mm. Conclusion: Despite the fact that this study was funded by Zimmer Biomet and only used Zimmer Biomet implants, robot, and navigation tools, the results of our in vitro study demonstrated that surgeons using this new surgical robot in total knee arthroplasty can perform highly accurate bone cuts to achieve the planned angles and resection thickness as measured using conventional navigation. (C) 2019 Elsevier Inc. All rights reserved.