Image-Free Robotic-Assisted Total Knee Arthroplasty Improves Implant Alignment Accuracy: A Cadaveric Study.

Abstract

Improving resection accuracy and eliminating outliers in total knee arthroplasty (TKA) is important to improving patient outcomes regardless of alignment philosophy. Robotic-assisted surgical systems improve resection accuracy and reproducibility compared to conventional instrumentation. Some systems require preoperative imaging while others rely on intraoperative anatomic landmarks. We hypothesized that the alignment accuracy of a novel image-free robotic-assisted surgical system would be equivalent or better than conventional instrumentation with fewer outliers. Forty cadaveric specimens were used in this study. Five orthopedic surgeons performed 8 bilateral TKAs each, using the VELYS Robotic-Assisted System (DePuy Synthes) and conventional instrumentation on contralateral knees. Pre-resection and postresection computed tomography scans, along with optical scans of the implant positions were performed to quantify resection accuracies relative to the alignment targets recorded intraoperatively. The robotic-assisted cohort demonstrated smaller resection errors compared to conventional instrumentation in femoral coronal alignment (0.63° ± 0.50° vs 1.39° ± 0.95°, P < .001), femoral sagittal alignment (1.21° ± 0.90° vs 3.27° ± 2.51°, P < .001), and tibial coronal alignment (0.93° ± 0.72° vs 1.65° ± 1.29°, P= .001). All other resection angle accuracies were equivalent. Similar improvements were found in the femoral implant coronal alignment (0.89° ± 0.82° vs 1.42° ± 1.15°, P= .011), femoral implant sagittal alignment (1.51° ± 1.08° vs 2.49° ± 2.10°, P= .006), and tibial implant coronal alignment (1.31° ± 0.84° vs 2.03° ± 1.44°, P= .004). The robotic-assisted cohort had fewer outliers (errors >3°) for all angular resection alignments. This invitro study demonstrated that image-free robotic-assisted TKA can improve alignment accuracy compared to conventional instrumentation and reduce the incidence of outliers.