Inverse Kinematic Alignment (iKA) and Gap Balancing (GB) aim to achieve a balanced TKA via component alignment. However, iKA aims to recreate the native joint line versus resecting the tibia perpendicular to the mechanical axis. This study aims to compare how two alignment methods impact 1) gap balance and laxity throughout flexion and 2) the coronal plane alignment of the knee (CPAK). Two surgeons performed 75 robotic assisted iKA TKA's using a cruciate retaining implant. An anatomic tibial resection restored the native joint line. A digital joint tensioner measured laxity throughout flexion prior to femoral resection. Femoral component position was adjusted using predictive planning to optimize balance. After femoral resection, final joint laxity was collected. Planned GB (pGB) was simulated for all cases posthoc using a neutral tibial resection and adjusting femoral position to optimize balance. Differences in ML balance, laxity, and CPAK were compared between planned iKA (piKA) and pGB. ML balance and laxity were also compared between piKA and final (fiKA). piKA and pGB had similar ML balance and laxity, with mean differences <0.4mm. piKA more closely replicated native MPTA (Native=86.9±2.8°, piKA=87.8±1.8°, pGB=90±0°) and native LDFA (Native=87.5±2.7°, piKA=88.9±3°, pGB=90.8±3.5°). piKA planned for a more native CPAK distribution, with the most common types being II (22.7%), I (20%), III (18.7%), IV (18.7%) and V (18.7%). Most pGB knees were type V (28.4%), VII (37.8%), and III (16.2). fiKA and piKA had similar ML balance and laxity, however fiKA was more variable in midflexion and flexion (p<0.01). Although ML balance and laxity were similar between piKA and pGB, piKA better restored native joint line and CPAK type. The bulk of pGB knees were moved into types V, VII, and III due to the neutral tibial cut. Surgeons should be cognizant of how these differing alignment strategies affect knee phenotype

Introduction. Although total knee arthroplasty (TKA) is generally considered successful, 16–30% of patients are dissatisfied. There are multiple reasons for this, but some of the most frequent reasons for revision are instability and joint stiffness. A possible explanation for this is that the implant alignment is not optimized to ensure joint stability in the individual patient. In this work, we used an artificial neural network (ANN) to learn the relation between a given standard cruciate-retaining (CR) implant position and model-predicted post-operative knee kinematics. The final aim was to find a patient-specific implant alignment that will result in the estimated post-operative knee kinematics closest to the native knee. Methods. We developed subject-specific musculoskeletal models (MSM) based on magnetic resonance images (MRI) of four ex vivo left legs. The MSM allowed for the estimation of secondary knee kinematics (e.g. varus-valgus rotation) as a function of contact, ligament, and muscle forces in a native and post-TKA knee. We then used this model to train an ANN with 1800 simulations of knee flexion with random implant position variations in the ±3 mm and ±3° range from mechanical alignment. The trained ANN was used to find the implant alignment that resulted in the smallest mean-square-error (MSE) between native and post-TKA tibiofemoral kinematics, which we term the dynamic alignment. Results. Dynamic alignment average MSE kinematic differences to the native knees were 1.47 mm (± 0.89 mm) for translations and 2.89° (± 2.83°) for rotations. The implant variations required were in the range of ±3 mm and ±3° from the starting mechanical alignment. Discussion. In this study we showed that the developed tool has the potential to find an implant position that will restore native tibiofemoral kinematics in TKA. The proposed method might also be used with other alignment strategies, such as to optimize implant position towards native ligament strains. If native knee kinematics are restored, a more normal gait pattern can be achieved, which might result in improved patient satisfaction. The small changes required to achieve the dynamic alignment do not represent large modifications that might compromise implant survivorship. Conclusion. Patient-specific implant position predicted with MSM and ANN can restore native knee function in a post-TKA knee with a standard CR implant

Introduction. Varus alignment in total knee replacement (TKR) results in a larger portion of the joint load carried by the medial compartment. [1]. Increased burden on the medial compartment could negatively impact the implant fixation, especially for cementless TKR that requires bone ingrowth. Our aim was to quantify the effect varus alignment on the bone-implant interaction of cementless tibial baseplates. To this end, we evaluated the bone-implant micromotion and the amount of bone at risk of failure. [2,3]. Methods. Finite element models (Fig.1) were developed from pre-operative CT scans of the tibiae of 11 female patients with osteoarthritis (age: 58–77 years). We sought to compare two loading conditions from Smith et al.;. [1]. these corresponded to a mechanically aligned knee and a knee with 4° of varus. Consequently, we virtually implanted each model with a two-peg cementless baseplate following two tibial alignment strategies: mechanical alignment (i.e., perpendicular to the tibial mechanical axis) and 2° tibial varus alignment (the femoral resection accounts for additional 2° varus). The baseplate was modeled as solid titanium (E=114.3 GPa; v=0.33). The pegs and a 1.2 mm layer on the bone-contact surface were modeled as 3D-printed porous titanium (E=1.1 GPa; v=0.3). Bone material properties were non-homogeneous, determined from the CT scans using relationships specific to the proximal tibia. [2,4]. The bone-implant interface was modelled as frictional with friction coefficients for solid and porous titanium of 0.6 and 1.1, respectively. The tibia was fixed 77 mm distal to the resection. For mechanical alignment, instrumented TKR loads previously measured in vivo. [5]. were applied to the top of the baseplate throughout level gait in 2% intervals (Fig.1a). For varus alignment, the varus/valgus moment was modified to match the ratio of medial-lateral force distribution from Smith et al. [1]. (Fig.1b). Results. For both alignments and all bones, the largest micromotion and amount of bone at risk of failure occurred during mid stance, at 16% of gait (Figs.2,3). Peak micromotion, located at the antero-lateral edge of the baseplate, was 153±32 µm and 273±48 µm for mechanical and varus alignment, respectively. The area of the baseplate with micromotion above 40 µm (the threshold for bone ingrowth. [3]. ) was 28±5% and 41±4% for mechanical and varus alignment, respectively. The amount of bone at risk of failure at the bone-implant interface was 0.5±0.3% and 0.8±0.3% for the mechanical and varus alignment, respectively. Discussion. The peak micromotion and the baseplate area with micromotion above 40 µm increased with varus alignment compared to mechanical alignment. Furthermore, the amount of bone at risk of failure, although small for both alignments, was greater for varus alignment. These results suggest that varus alignment, consisting of a combination of femoral and tibial alignment, may negatively impact bone ingrowth and increase the risk of bone failure for cementless tibial baseplates of this TKR design

Introduction. Soft tissue releases are often required to correct deformity and achieve gap balance in total knee arthroplasty (TKA). However, the process of releasing soft tissues can be subjective and highly variable and is often perceived as an ‘art’ in TKA surgery. Releasing soft tissues also increases the risk of iatrogenic injury and may be detrimental to the mechanically sensitive afferent nerve fibers which participate in the regulation of knee joint stability. Measured resection TKA approaches typically rely on making bone cuts based off of generic alignment strategies and then releasing soft tissue afterwards to balance gaps. Conversely, gap-balancing techniques allow for pre-emptive adjustment of bone resections to achieve knee balance thereby potentially reducing the amount of ligament releases required. No study to our knowledge has compared the rates of soft tissue release in these two techniques, however. The objective of this study was, therefore, to compare the rates of soft tissue releases required to achieve a balanced knee in tibial-first gap-balancing versus femur-first measured-resection techniques in robotic assisted TKA, and to compare with release rates reported in the literature for conventional, measured resection TKA [1]. Methods. The number and type of soft tissue releases were documented and reviewed in 615 robotic-assisted gap-balancing and 76 robotic-assisted measured-resection TKAs as part of a multicenter study. In the robotic-assisted gap balancing group, a robotic tensioner was inserted into the knee after the tibial resection and the soft tissue envelope was characterized throughout flexion under computer-controlled tension (fig-1). Femoral bone resections were then planned using predictive ligament balance gap profiles throughout the range of motion (fig-2), and executed with a miniature robotic cutting-guide. Soft tissue releases were stratified as a function of the coronal deformity relative to the mechanical axis (varus knees: >1° varus; valgus knees: >1°). Rates of releases were compared between the two groups and to the literature data using the Fischer's exact test. Results. The overall rate of soft tissue release was significantly lower in the robotic gap-balancing group, with 31% of knees requiring one or more releases versus 50% (p=0.001) in the robotic measured resection group and 66% (p<0.001) for conventional measured resection (table-1) [1]. When comparing as a function of coronal deformity, the difference in release rates for robotic gap-balancing was significant when compared to the conventional TKA literature data (p<0.0001) for all deformity categories, but only for varus and valgus deformities for robotic measured resection with the numbers available (varus: 33% vs 50%, p=0.010; neutral 11% vs 50%, p=0.088, valgus 27% vs 53%, p=0.048). Discussion. Robotic-assisted tibial-first gap-balancing techniques allow surgeons to plan and adjust femoral resections to achieve a desired gap balance throughout motion, prior to making any femoral resections. Thus, gap balance can be achieved through adjustment of bone resections, which is accurate to 1mm/degree with robotics, rather than through manual releasing soft tissues which is subjective and less precise. These results demonstrated that the overall rate of soft tissue release is reduced when performing TKA with predictive gap-balancing and a robotic tensioning system. For any figures or tables, please contact authors directly

Introduction. Variation in resection thickness of the femur in Total Knee Arthroplasty (TKA) impacts the flexion and extension tightness of the knee. Less well investigated is how variation in patient anatomy drives flexion or extension tightness pre- and post- operatively. Extension and flexion stability of the post TKA knee is a function of the tension in the ligaments which is proportional to the strain. This study sought to investigate how femoral ligament offset relates to post-operative navigation kinematics and how outcomes are affected by component position in relation to ligament attachment sites. Method. A database of TKA patients operated on by two surgeons from 1-Jan-2014 who had a pre-operative CT scan were assessed. Bone density of the CT scan was used to determine the medial and lateral collateral attachments. Navigation (OmniNav, Raynham, MA) was used in all surgeries, laxity data from the navigation unit was paired to the CT scan. 12-month postoperative Knee Osteoarthritis and Outcome Score (KOOS) score and a postoperative CT scan were taken. Preoperative segmented bones and implants were registered to the postoperative scan to determine change in anatomy. Epicondylar offsets from the distal and posterior condyles (of the native knee and implanted components), resections, maximal flexion and extension of the knee and coronal plane laxity were assessed. Relationships between these measurements were determined. Surgical technique was a mix of mechanical gap balancing and kinematically aligned knees using Omni (Raynham, MA) Apex implants. Results. 119 patients were identified in the database. 60% (71) were female and the average age was 69.0 years (+/− 8.1). The average distal femoral bone resection was 7.5 mm (+/− 1.6) medially and 5.4 mm (+/− 2.1) laterally, and posterior 10.2 mm (+/− 1.7) medially and 8.4 mm (+/− 1.8) laterally, with implant replacement thicknesses 9 mm distally and 11 mm posterior. Maximum flexion of the knee post implantation was 121.5° (+/− 8.1) from a preoperative value of 117.9° (+/− 9.5). Change in the collateral ligament offsets brought on by surgery had significant correlations with several laxity and flexion measures. Increase in the posterior offset of the medial collateral attachment brought on by surgery was shown to decrease the maximum flexion attained (coefficient = −0.53, p < 0.001), Figure 1. Increased distal medial offset post-operatively compared to the posterior offset is significantly correlated with improved KOOS pain outcomes (coefficient = 0.23, p = 0.01). Similarly, a decrease in the distal offset of the lateral collateral ligament increased the coronal plane laxity in extension (coefficient = 0.37, p < 0.001), while the posterior lateral resection was observed to correlate with postoperative coronal laxity in flexion (coefficient = 0.42, p < 0.001). Conclusions. Accounting for variation in ligament offset during surgically planning may improve balancing outcomes. Although new alignment approaches, such as kinematic alignment, have been able to demonstrate improvements in short term outcomes, elimination of postoperative dissatisfaction has not been achieved. The interaction of an alignment strategy with a given patient's specific anatomy may be the key to unlocking further TKA patient outcome gains

Introduction. Mechanically aligned total knee arthroplasty(TKA) relies on restoring the hip-knee-ankle angle of the limb to neutral or as close to a straight line as possible. This principle is based on studies that suggest limb and knee alignment is related long term survival and wear. For that cause, there has been recent attention concerning computer-assisted TKA and robot is also one of the most helpful instruments for restoring neutral alignment as known. But many reported data have shown that 20% to 25% of patients with mechanically aligned TKA are dissatisfied. Accordingly, kinematically aligned TKA was implemented as an alternative alignment strategy with the goal of reducing prevalence of unexplained pain, stiffness, and instability and improving the rate of recovery, kinematics, and contact forces. So, we want to report our extremely early experience of robot-assisted TKA planned by kinematic method. Materials and Methods. This study evaluated the very short term results (6 weeks follow up) after robot-assisted TKA aligned kinematically. 50 knees in 36 patients, who could be followed up more than 6 weeks after surgery from December 2014 to January 2015, were evaluated prospectively. The diagnosis was primary osteoarthritis in all cases. The operation was performed with ROBODOC (ISS Inc., CA, USA) along with the ORTHODOC (ISS Inc., CA, USA) planning computer. The cutting plan was made by single radius femoral component concept, each femoral condyles shape-matched method along the transverse axis using multi-channel CT and MRI to place the implant along the patient's premorbid joint line. Radiographic measurements were made from long bone scanograms. Clinical outcomes and motion were measured preoperatively and 6 weeks postoperatively. Results. The range of motion increased from preoperative mean 113.4 (±5.4, 85 to 130) to postoperative mean 127.3 (±7.4, 90 to 140) at last follow up. The mean knee score and functional score improved from 35.4 (±10.3, 10 to 55) and 30.1 (±7.7, 10 to 60) before surgery to 88.6 (±5.8, 60 to 100) and 90.7 (±9.6, 60 to 100) at last follow up. The WOMAC score was improved from 52(±15.5) to 20(±14.8) at last follow up. The postoperative Hip-knee-ankle alignment was −1.3±2.8. The femoral component was 2.1 valgus and tibial component was 2.8 varus along the mechanical axis in coronal plane. There were no complications and failures. Conclusion. On the basis of our results, we are cautiously optimistic about robot-assisted TKA by kinematically alignment. More anatomic alignment of the implant can be associated with better flexion and better clinical outcomes scores in the kinematically aligned method in our thinking. But, at this starting point, more comparative studies with mechanical aligned group are needed and we must explore about implant survivalship issues and implant loading issues in dynamic and static condition that someone is worrying about. If the problem can be solved, there is no use worrying about it in our thinking. And what is more, the robot-assisted surgery will be very useful especially in those cases of severely deformed knees and distorted anatomy to be aligned kinematically

INTRODUCTION. While standard instrumentation tries to reproduce mechanical axes based on mechanical alignment guides, a new “shape matching” system derives its plan from kinematic measurements using pre-operative MRIs. The current study aimed to compare the resultant alignment in a matched pair cadaveric study between the Shape Match and a standard mechanical system. METHODS. A prospective series of Twelve (12) eviscerated torso's were acquired for a total of twenty four (24) limb specimens that included intact pelvises, femoral heads, knees, and ankles. The cadavers received MRI-scans, which were used to manufacture the Shape Match cutting guides. Additionally all specimen received “pre-operative” CT-scans to determine leg axes. Two (2) investigating surgeons performed total knee arthroplasties on randomly chosen sides by following the surgical technique using conventional instruments. On the contralateral sides, implantation of the same prosthesis was done using the Kinematic Shape Match Cutting Guides. A navigation system was used to check for leg alignement. Implant alignement was determined using post-operative CT-scans. For statistical analysis SPSS was used. RESULTS. In measurements using the navigation system, the overall alignment of the leg showed no significant differences between the two tested systems. This was also found in the CT-Measurements. In the Shape Match group the difference between the planned and the final implantation regarding overall limb alignment ranged between −0,5° (valgus) and 6° varus (p=0,518; CI −1,97°/1,05°). The leg alignement in the conventional group ranged between −2,5° and 13° varus (p=0,176; CI −4,93°/1,02). DISCUSSION AND CONCLUSION. As expected, the two compared system employ different alignment strategies, which reflected in variations of the combinations of the three-dimensional component position on the femur and the tibia. These different strategies result in overall leg alignment that compares well between the two different methods, with fewer outliers in the Shape Match group