Robotic arm-assisted total knee arthroplasty improves preoperative planning and intraoperative decision-making

Surgeon-Applied Stress and a Ligament Tensor Instrument Provide a Similar Assessment of Preresection Flexion Laxity During Robotic Total Knee Arthroplasty

Background

Robotic-assisted total knee arthroplasty (RA-TKA) allows surgeons to perform intraoperative soft tissue laxity assessments prior to bone resections and is used to alter resections to achieve gap balance. This study compared 2 techniques for flexion gap laxity assessment during RA-TKA.

Methods

A prospective study of 50 primary RA-TKAs performed by a single surgeon was conducted between February and October 2023. Following full exposure, anterior tibial dislocation, and osteophyte removal, maximal medial and lateral compartment flexion laxity was quantified to the nearest 0.5 mm by the robotic system using a dynamic, surgeon-applied stress (SURGEON). This data was used to plan a balanced flexion gap by adjusting the femoral component size, rotation, and anterior-posterior translation. Flexion laxity was quantified again after distal femoral and proximal tibial resections using a ligament tensor instrument (TENSOR). These new data were used to plan for the same desired flexion gap using the same variables. Paired-samples t-tests and a simple linear regression were used for analysis.

Results

Both methods produced near-identical recommendations for femoral component sizing (mean deviation 0.06 sizes, range -1 to +1 size; P = .569), rotation (deviation mean 1.0°, range -3.0° to +3.0°; P = .741), and anterior-posterior translation (deviation mean 0.13 mm, range -0.5 to +0.5 mm, P = .785). SURGEON femoral component rotation predicted TENSOR rotation (R2 = 0.157; 95% confidence interval = 0.124, 0.633; P = .004).

Conclusions

Assessing flexion laxity with a surgeon-applied stress vs a ligament tensor produced near-identical laxity data in RA-TKA, suggesting surgeons may comfortably choose either technique as a reliable method.

Level of Evidence

Level III.

Assessment of preoperative planning and intraoperative accuracy of the AIKNEE system for total knee arthroplasty

BACKGROUND

The aim of this retrospective study was to evaluate the effectiveness and accuracy of the AIKNEE system in preoperative planning and intraoperative alignment for total knee arthroplasty (TKA).

METHODS

A total of 64 patients were planned preoperatively by the AIKNEE system, including the measurement of mechanical femorotibial angle (mFTA), lateral distal femoral angle (LDFA), and medial proximal tibial angle (MPTA) using three-dimensional reconstructed images. Intraoperatively, the actual prosthesis size and alignment were compared to the planned parameters. Postoperative outcomes, including pain levels, range of motion (ROM), and Knee Scoring System (KSS) scores, were assessed after surgery. Statistical analyses were performed to evaluate the correlation between alignment deviations and postoperative function.

RESULTS

The AIKNEE system accurately predicted the prosthesis size in thirty-one of femoral cases (48%) and forty-seven of tibial cases (73%). Deviations of mFTA, LDFA, and MPTA from the target value were within 3° in 88%, 92%, and 95% of cases, respectively. A significant improvement was observed in postoperative pain, ROM, and KSS scores (p < 0.001). Correlation analysis revealed that greater deviations in mFTA and LDFA were associated with increased pain (p = 0.004, 0.047) and lower KSS scores (p = 0.027).

CONCLUSION

The AIKNEE system demonstrated promising results in predicting prosthesis size and achieved alignment within the desired range in a majority of cases. Postoperative outcomes, including pain levels and functional improvement, were favorable.

Evaluating the accuracy of a new robotically assisted system in cadaveric total knee arthroplasty procedures

BACKGROUND

Robot-assisted total knee arthroplasty (TKA) has been shown to facilitate high-precision bone resection, which is an important goal in TKA. The aim of this cadaveric study was to analyze the accuracy of the target angle and bone resection thickness of a recently introduced robotic TKA system.

METHODS

This study used 4 frozen cadaveric specimens (8 knees), 2 different implant designs, navigation, and a robotic system. The 4 surgeons who participated in this study were trained and familiar with the basic principles and operating procedures of this system. The angle of the bone cuts performed using the robotic system was compared with the target angles from the intraoperative plan. For each bone cut, the resection thickness was recorded and compared with the planned resection thickness.

RESULTS

The mean angular difference for all specimens was less than 1°, and the standard deviation was less than 2°. The mean difference between the planned and measured angles was close to 0 and not significantly different from 0 except for the difference in the frontal tibial component angle, which was 0.88°. The mean difference in the hip-knee-ankle axis angle was - 0.21°± 1.06°. The mean bone resection difference for all specimens was less than 1 mm, and the standard deviation was less than 0.5 mm.

CONCLUSIONS

The results of the cadaveric experimental study showed that the new TKA system can realize highly accurate bone cuts and achieve planned angles and resection thicknesses. Despite the limitations of small sample sizes and large differences between cadaveric and clinical patients, the accuracy of cadaveric experiments provides strong support for subsequent clinical trials.

Improved intra-operative decision making in RA-TKR with the help of pre-operative CT scan

Background

Robotic-assisted total knee replacement (RA-TKR) is a significant advancement in orthopedic surgery, but intra-operative decision-making remains challenging. Pre-operative imaging techniques, particularly CT scans, have gained momentum, providing insights into the patient's anatomy, improving implant positioning and alignment. However, further research is needed to explore their influence on RA-TKR planning and execution.

Materials and methods

The hospital based cross-sectional study was conducted in Orthopedics department of Sparsh Speciality Hospital, Bangalore & Sunshine Hospital, Hyderabad. A total of 1020 participants in the age group over 50 years during the study period were included based on convenient sampling. The axial CT images were taken preoperatively and RA-TKA was done for all the patients.

Results

The study participant's average age was 64.01 ± 7.13. Out of 1020 patients 259 (24.4%) were males and 761 (74.6%) were females. The median femoral, tibia and Polyethylene predicted and the actual component were same with the side of surgery and BMI. The median femoral predicted actual component was significantly higher among the age category of more than 80 years when compared to other age groups. The median femoral, tibia and Polyethylene predicted was higher in males when compared to females.

Conclusion

Pre-operative CT scans enhance RA-TKR procedures by providing precise anatomical insights, enhancing implant placement, and identifying potential issues, improving surgical outcomes and patient satisfaction.

High three-dimensional accuracy of component placement and lower limb alignment using a robotic arm-assisted system and gap-balancing instrument in total knee arthroplasty

PURPOSE

It was hypothesized that robotic arm-assisted total knee arthroplasty (RA-TKA) using additionally a gap-balancing instrumentation will show high accuracy in executing the planning in femoral and tibial component placement throughout the range of knee motion (ROM) during TKA surgery.

METHODS

Prospectively collected data were analysed for patients undergoing RA-TKA. A cruciate retaining cemented design was implanted using the MAKO® robotic system. Lower limb alignment at 0°, 30°, 45°, 60° and 90° of flexion was recorded at the beginning of surgery and finally after implantation of the components. A ligament tensioner was inserted after tibial precut to measure the extension and flexion gap, and final component placement was planned based on 3D CT images. Femoral and tibial component placement was measured in all three planes.

RESULTS

A total of 104 patients were included (mean age 69.4 ± 9 years; 44 male, 60 female). The difference in component placement after planning and final implantation showed less valgus of 0.7° ± 1.4° (p < 0.001), less external rotation of 0.6° ± 1.9° (p = 0.001) and less flexion of 0.9° ± 1.8° (p < 0.001) for the femoral component. The tibial component was placed in more varus of 0.2° ± 0.9° (p = 0.056) and more posterior slope of 0.5° ± 0.9° (p < 0.001). The lower limb alignment in extension was 4.4° ± 5.2° of varus of the native knee and changed to 1.2° ± 1.9° of varus after TKA (p < 0.01).

CONCLUSION

Robotic-assisted TKA helps to achieve the target of alignment and component placement very close to the planning. It allows optimal component placement of off-the-shelf implants respecting patient's specific anatomy.

LEVEL OF EVIDENCE

Level II.

Predictability of implant sizes during cruciate-retaining total knee arthroplasty using an image-free hand-held robotic system

The use of appropriately sized implants is critical for achieving optimal gap balance following total knee arthroplasty (TKA). Inappropriately sized implants could result in several complications. Robot-assisted TKA (RA-TKA) using CT-based pre-operative planning predicts implant sizes with high accuracy. There is scant literature describing the accuracy of image-free RA-TKA in predicting implant sizes. The purpose of this study was to assess the accuracy of an image-free robotic system in predicting implant sizes during RA-TKA. Patients who underwent cruciate-retaining RA-TKA for primary osteoarthritis, using an image-free hand-held robotic system were studied. The predicted and implanted sizes of the femoral component, tibial component and polyethylene insert, for 165 patients, were recorded. Agreement between robot-predicted and implanted component sizes was assessed in percentages, while reliability was assessed using Cohen's weighted kappa coefficient. The accuracy of the robotic system was 63% (weighted-kappa = 0.623, P < 0.001), 94% (weighted-kappa = 0.911, P < 0.001) and 99.4% (weighted-kappa = 0.995, P < 0.001), in predicting exact, ± 1 and ± 2 sizes of the femoral component, respectively. For the tibial component, an accuracy of 15.8% (weighted-kappa = 0.207, P < 0.001), 55.8% (weighted-kappa = 0.378, P < 0.001) and 76.4% (weighted-kappa = 0.568, P < 0.001) was noted, for predicting exact, ± 1 and ± 2 sizes respectively. An accuracy of 88.5%, 98.2% and 100%, was noted for predicting exact, ± 1 and ± 2 sizes of the polyethylene insert respectively. Errors in predicting accurate implant sizes could be multi-factorial. Though the accuracy of image-free RA-TKA with respect to alignment and component positioning is established, the surgeon's expertise should be relied upon while deciding appropriate implant sizes.

Robotic Arm-Assisted Total Knee Arthroplasty: Anatomical Alignment and Mid-Term Outcomes from the First Cohort Originating in Greece

Robotic arm-assisted total knee arthroplasty (RATKA) represents a haptic assistive robotic arm used for bone preparation. The purpose of this study was to present implant survivorship, complications and evaluate patients' satisfaction, clinical and functional outcome of RATKA with a minimum of 1-year follow-up. The Oxford Knee Score was recorded preoperatively and at last follow-up. Patients' satisfaction rates, as well as complications and re-operations were studied. Anatomical alignment including varus, valgus deformities and flexion, extension, pre-and postoperatively were evaluated. A total of 156 patients with mean age = 71.9 years were included in the study. The mean follow-up was 35.7 months, while one revision was performed due to infection. Statistically significant improvement of the Oxford Knee Score, as well as of the knee alignment deformities were recorded, while 99.4% of patients reported to be "very satisfied" or "satisfied" with the procedure. RATKA seems to be a safe, as well as reproductible procedure at short-and mid-term follow-up, while the accurate implant positioning may lead to favorable long-term outcomes.

Tracing the evolution of robotic-assisted total knee arthroplasty: a bibliometric analysis of the top 100 highly cited articles

Robotic-assisted surgical systems hold promise in enhancing total knee arthroplasty (TKA) outcomes and patients' quality of life. This study aims to comprehensively analyze the literature on robot-assisted total knee arthroplasty (r-TKA), providing insights into its current development, clinical application, and research trends. A systematic search was conducted in the Web of Science Core Collection (WOSCC) to identify relevant articles. Data were collected from the top 100 highly cited articles. Article evidence levels were assessed following established guidelines. Statistical analyses and visualizations were performed to reveal publication trends, citations, research hotspots, and collaborative networks. The analysis covered 100 highly cited articles meeting the research criteria, with a focus on the last five years. The United States emerged as a major contributor, with most publications and citations in the Journal of Knee Surgery and Knee Surgery Sports Traumatology Arthroscopy. Research priorities revolved around clinical outcomes, accuracy, and alignment of r-TKA. Notably, higher evidence levels correlated with more citations, indicating greater attention. Interest in and research on r-TKA is steadily increasing, with a few countries at the forefront of these endeavors. While numerous studies have already reported short- to medium-term follow-up results, it is crucial to conduct longer-term investigations to gain a more comprehensive understanding of the clinical benefits that r-TKA offers compared to conventional techniques. Through ongoing research and a greater embrace of robotic technology, we can continue to improve the quality of life for patients undergoing knee arthroplasty.

Overview of the different personalized total knee arthroplasty with robotic assistance, how choosing?

Current limitations in total knee arthroplasty (TKA) function and patient satisfaction stimulated us to question our practice. Our understanding of knee anatomy and biomechanics has evolved over recent years as we now consider that a more personalized joint reconstruction may be a better-targeted goal for TKA. Implant design and surgical techniques must be advanced to better reproduce the anatomy and kinematics of native knees and ultimately provide a forgotten joint. The availability of precision tools as robotic assistance surgery can help us recreate patient anatomy and ensure components are not implanted in a position that may compromise long-term outcomes. Robotic-assisted surgery is gaining in popularity and may be the future of orthopedic surgery. However, moving away from the concept of neutrally aligning every TKA dogma opens the door to new techniques emergence based on opinion and experience and leads to a certain amount of uncertainty among knee surgeons. Hence, it is important to clearly describe each technique and analyze their potential impacts and benefits. Personalized TKA techniques may be classified into 2 main families: unrestricted or restricted component orientation. In the restricted group, some will aim to reproduce native ligament laxity versus aiming for ligament isometry. When outside of their boundaries, all restricted techniques will induce anatomical changes. Similarly, most native knee having asymmetric ligaments laxity between compartments and within the same compartment during the arc of flexion; aiming for ligament isometry induces bony anatomy changes. In the current paper, we will summarize and discuss the impacts of the different robotic personalized alignment techniques, including kinematic alignment (KA), restricted kinematic alignment (rKA), inverse kinematic alignment (iKA), and functional alignment (FA). With every surgical technique, there are limitations and shortcomings. As our implants are still far from the native knee, it is primordial to understand the impacts and benefits of each technique. Mid to long data will help us in defining the new standards.