Active robotics for total knee Arthroplasty (TKA) uses a CAD-CAM approach to plan the correct size and placement of implants and to surgically achieve planned limb alignment. The TSolution One Total Knee Application (THINK Surgical Inc., Fremont, CA) is an open-implant platform, CT-based active robotic surgical system. A multi-center, prospective, non-randomized clinical trial was performed to evaluate the safety and effectiveness of robotic-assisted TKA using the TSolution One Total Knee Application. This report details the findings from the IDE. Inclusion criteria for patients receiving robotic TKA were: primary unilateral TKA; Kellgren-Lawrence OA grade 3 or 4; BMI < 40 kg/m2; coronal plane deformity < 20° varus; sagittal flexion contracture < 15°. In addition to monitoring all adverse events (AE), a pre-defined list of relevant major AEs were specifically identified to evaluate safety (Healy et al, 2013): medial collateral ligament injury; extensor mechanism disruption; neural deficit; periprosthetic fracture; patellofemoral dislocation; tibiofemoral dislocation; and vascular injury. Bleeding complications were also assessed. Malalignment rate, defined as the percentage of patients with more than a ± 3° difference in varus-valgus alignment from the preoperative plan, was used to determine accuracy of the active robotic system. Knee Society Scores (KSS) and Short Form 12 (SF-12) Health Surveys were assessed as clinical outcome measures. For each outcome, results were compared to published values associated with manual TKA.Introduction
Methods
From pre-operative planning to final implant cementation, total knee arthroplasty (TKA) preparation is a succession of many individual steps, each presenting potential sources of error that can result in devices being implanted outside the targeted range of alignment. This study assessed alignment discrepancy occurring during different TKA steps using an image-free computer-assisted orthopaedic surgery (CAOS) guidance system (Exactech GPS, Blue-Ortho, Grenoble, FR) in normal and abnormal mechanical axis. We used a commercially available artificial leg (MITA trainer leg M-00058, Medical Models, Bristol, UK) able to receive (neutral / varus / valgus) knee inserts simulating the proximal tibia and distal femur. A pre-surgical profile was established to define resection parameters for the proximal tibial and distal femoral cuts (Figure 1A). Data from the guidance system were collected at three separate steps: (1) cutting block adjusted but not pinned to the bone (Figure 1B), (2) cutting block adjusted and pinned to the bone (Figure 1C), and (3) after the cuts were checked (Figure 1D). These data were then compared to the resection target parameters to track potential dispersions occurring during the process. Due to the amount of data (i.e., four studied resection parameters per bone, three operative steps, and three knee model types), the authors introduced an “error index”, which was a unitless indication of overall error magnitude obtained by averaging the absolute values of all linear and angular measurement errors. Due to knee model dimensions (∼55 mm), the authors equally considered linear and angular measurement values (i.e., 1 mm equivalent to 1°).Introduction
Materials and methods
An emerging consensus in the surgical specialties is that skill acquisition should be more emphasized during surgical training.1 This study was an attempt to evaluate the effects of repetitive practices using an image-free computer-assisted orthopaedic surgery (CAOS) guidance system (Exactech GPS, Blue-Ortho, Grenoble, FR) on both technical and cognitive skills. A senior knee replacement surgeon with limited previous experience with the CAOS system performed a series of consecutive simulated knee surgeries using a commercially available artificial leg (MITA trainer leg M-00058, Medical Models, Bristol, UK). In order to assess the effects repetitive practice has on technical skills, we evaluated two indexes: Error index: A unitless indication of overall error magnitude obtained by averaging the absolute values of all linear and angular measurement differences between targeted and checked cuts. Time index: An indication of the time required to acquire landmarks, adjust the custom blocks, and make cuts. In order to assess the effect repetitive practice has on cognitive skills, we evaluated the number of times the surgeon elected to deviate from pre-surgical planning or re-acquire landmarks. We evaluated these parameters for three chronological and consecutive groups of simulated surgeries: Group A (knee models #1 to #10), Group B (knee models #11 to #20), and Group C (knee models #21 to #28).Introduction
Materials and methods
Clinical outcomes for total knee arthroplasty (TKA) are especially sensitive to lower extremity alignment and implant positioning.1 The use of computer-assisted orthopaedic surgery (CAOS) can improve overall TKA accuracy.2 This study assessed the accuracy of an image-free CAOS guidance system (Exactech GPS, Blue-Ortho, Grenoble, FR) in both a synthetic leg with a normal mechanical axis and legs with abnormal mechanical axis. A high-resolution 3D scanner (Comet L3D, Steinbichler, Plymouth, MI) was used to scan varus-deformed (n=12), neutral (n=12), and valgus-deformed (n=4) knee inserts (Mita M-00566, M-00598, M-00567; respectively, Medical Models, Bristol, UK) and collect pre-identified anatomical landmarks prior to using the models to simulate knee surgery. The image-free CAOS guidance system was then used to acquire the same landmarks. After adjusting the position and orientation of the cutting block to match the targets, bone resections were performed, and the knee models were re-scanned. The 3D scans made before and after the cuts were overlaid and the resection parameters calculated using the pre-identified anatomical landmark data and advanced software (UG NX, Siemens PLM, Plano, TX). Data sets obtained from the 3D scanner (see Figure 1A) were compared with data sets from the guidance system (see Figure 1B). Given the accuracy of the 3D scanner (<50μm), its measurements were used as the baseline for assessing CAOS system error.Introduction
Materials and methods
