Inter- and intra-operator variability in ligament balance measurements in total knee arthroplasty with the robotic navigation system (ROSA®): in vivo study

Inter- and intra-operator variability in ligament balance measurements in total knee arthroplasty with the robotic navigation system (ROSA®): in vivo study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Inter- and intra-operator variability in ligament balance measurements in total knee arthroplasty with the robotic navigation system (ROSA®): in vivo study Johnatan Everaert, Esfandiar Chahidi, Maarten Ulrix, Arnaud Delafontaine, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6252951/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Purpose: This study quantifies the reproducibility of soft tissue laxity and gap measurements under manual stress, and implant positioning planning using the imageless ROSA® robotic system, by comparing a senior high-volume surgeon with a low-volume resident. Methods: In this single-center prospective study, 17 patients undergoing robotic-assisted total knee arthroplasty were evaluated. Intra- and inter-operator variability was assessed by recording intraoperative measurements and planning outcomes using a standardized protocol for functional alignment (FA). Results: Good-to-excellent reproducibility in soft tissue and gap assessments is demonstrated by both intra- and inter-operator measurements. Minor differences in planning parameters—including stylus height, femoral implant flexion, and the distal femoral cut—are observed, likely due to subjective high-volume surgeon adjustments. Conclusion: High reproducibility in soft tissue measurements and surgical planning across surgeons with different experience levels is shown by the ROSA® robotic system, while flexibility for individualized surgical strategies is retained. Figures Figure 1 Figure 2 Introduction Despite improvements in prosthetic designs and alignment techniques, up to 20% of patients still report knee pain or are unsatisfied with TKA [1, 2], leading sometimes to revision arthroplasty. Advances in technology seek to tackle these challenges, but the value of these innovations is still a topic of controversy. The introduction of robotic-assisted total knee arthroplasty (RA-TKA) aims to enhance the accuracy of surgical bone resection and alignment, optimize component positioning, minimize soft-tissue damage, enable ligament balancing of the knee (for surgeons who incorporate this approach into their surgical philosophy), and facilitate the tailored placement of standard off-the-shelf implants according to the patient’s specific parameters [3–5]. However, while some studies confirm that robotic assistance has indeed led to more accurate component positioning [4, 6–11], other studies indicate that its effects on clinical outcomes, patient satisfaction, and long-term survivorship of implants remain to be fully validated [6, 9, 12–17]. The increasing use of robotic systems in TKA [18], is also driven by their ability to improve the reproducibility of surgeries, allowing surgeons of varying experience levels to achieve alignment goals with accuracy and precision comparable to more established surgeons [5], while offering a relatively short learning curve. [19] The robot is therefore used as a precision tool, but its accuracy depends on two main factors: the intrinsic precision of the robot itself and the quality of data acquired by the surgeon. To achieve bone resection, implant size, and implant positioning, the intraoperative assessment of bony landmarks and clinically significant parameters, like the soft tissue balancing in flexion and extension, are critical. This step is one of the most important in the operating room, as all following surgical actions are based on this information. Thus, if any of this information is incorrect, it will result in the well-known "garbage in, garbage out" effect [20], resulting in poor planning and consequently wrong positioning of the implants (garbage out). It is therefore crucial that the user's proficiency with the robot minimally affects the quality of the acquisitions (garbage in). Given the importance of accurate intraoperative measurements, it is essential to assess how variability in surgeon experience affects the precision of the system. This study aims to evaluate intra- and inter-operator variability according to surgeon experience during intraoperative gap assessment and bone-cut planning using the imageless ROSA® robotic system. The authors hypothesize that good inter- and intra-operator variability for both surgical phases will be observed, regardless of surgeon experience. Materials and Methods Ethical Approval This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the local Ethics Committee. Study Design Between July and September 2023, seventeen robotic-assisted total knee arthroplasty (RA-TKA) was performed by the senior surgeon in collaboration with the same resident in a single-center prospective study. The posterior stabilized (PS) Persona® implant was placed with the assistance of the semi-active robotic system ROSA® (Software and hardware by Zimmer Biomet, Warsaw, IN). Surgical indications consisted of symptomatic tricompartmental knee osteoarthritis in adult patients (>18 years), with no history of previous knee surgery. Contraindications were defined as post-traumatic pathology or inflammatory arthritis, revision arthroplasty, as well as neurological or muscular pathologies affecting the lower limbs. The severity of osteoarthritis and/or axial deviation did not influence patient selection for the study. Patients adhered to the institution's standard care pathway until the day of surgery. Data were collected only when the resident assisted the compared senior surgeon. The ROSA® software allowed data to be exported in the form of an Excel file, while screenshots were included in ".jpeg" format to document the planning process. Data were associated with the patient through an alphanumeric code created by the Zimmer-Biomet database software before the surgery. Surgical Step The operation began with the classic setup of the ROSA® robotic system and surgical parapatellar knee approach. Once the knee was exposed and osteophytes were removed, navigation trackers were conventionally placed by the senior surgeon on the femur and tibia. Only the imageless mode was used on the robotic system, which meant that planning was done intraoperatively based on the acquisition. All bony landmarks necessary to map the joint were collected, which determined the measurement of the native knee coronal alignment with Hip-Knee-Ankle (HKA) angle, range of motion, and ligament balancing, all of which were recorded by the software. Measurements Acquisition Ligament balance measurements were performed alternately by the senior surgeon and the resident. All measurements were conducted following the same routine protocol. Measurements were obtained through manual stress in valgus and varus of the knee (Figure 1) at 0° and 90° of flexion, defining joint gaps (Figure 2). Three evaluations were required in total to assess inter- and intra-operator variability. Inter-operator variability was evaluated between the senior surgeon and the resident, while intra-operator variability was assessed with a second measurement by the senior. To avoid confirmation bias, specific measures were implemented throughout the evaluation process. Initially, during the first assessment (inter-operator), the senior surgeon conducted the evaluation without providing visual access to the monitor to the resident. Similarly, during the third evaluation (intra-operator), the senior surgeon abstained from having visual access to the monitor. These steps were taken to ensure impartiality and minimize the potential influence of preconceived notions on the evaluation outcomes. Planification In line with the philosophy of the similar ligament balancing protocol adopted by the operator, the sequence of modification of different parameters followed the same steps (Annex 1). Measurements of bone cuts as well as the sizing of the femoral implant and polyethylene (Figure 2) were recorded at the end of each laxity assessment and planning. The size of the tibial implant was not retained due to default parameters in the software, which suggested a default size type in imageless procedures. Final bone cuts were performed based on the measurements from the senior surgeon's last ligament balance and planning, which did not alter the initial patient care. No additional or exceptional interventions were performed. Indeed, during acquisition, measurements were repeatedly reassessed as part of the routine, which did not substantially increase operative time and therefore did not pose an increased risk to the patient. Statistical analysis Inter-rater and intra-rater reliabilities, using two-way random intra- and inter-class correlation coefficients (ICCs), were calculated between all measured variables in order to test the reproducibility and repeatability of the proposed technique. The ICC (calculated using k-Rating, Absolute-Agreement, 2-Way Random-Effects Model) is by definition measured in a range from 0 to 1, with a value close to 1 indicating better agreement: an ICC > 0.75 indicates excellent agreement, while an ICC > 0.60 is taken as a threshold for a good agreement. The R software (R Core Team, 2021), version 4.2.0. was used to produce the results. Results Seventeen patients were recruited for this study (3 males and 14 females). The average age was 53.47 ± 8.30 years. Of the 17 knees evaluated, 7 were on the left side and 10 on the right, with 14 classified as varus and 2 as valgus (Table 1). Acquisition The ICCs resulting from the acquisition step, including the Hip-Knee-Ankle (HKA) angle, range of motion (ROM), and ligament balancing (coronal laxity and flexion/extension spaces), are reported in Table 2. All ICC values indicate excellent agreement (>0.75), demonstrating high reproducibility, with consistent results observed for both the senior surgeon (average intra-observer variability < 1° for all flexion angles) and the resident (average inter-observer variability < 1° for all flexion angles), except for the laxity profile. The ICC values of the laxity profile (total range of laxity, expressed in absolute values, based on the extreme values obtained during varus and valgus manual stress) show good inter-rater agreement (>0.60) in flexion and extension and good intra-rater agreement (>0.60) in extension only. Planification The ICC values from the planning step, including the estimation of bone resections, implant positioning in varus/valgus, and adjustments for femoral component flexion and rotation relative to the posterior condylar axis (PCA), indicate good (>0.60) to excellent (>0.75) agreement, except for the stylus height, femoral implant flexion, and medial distal femoral cut (Table 3). The polyethylene size is the only parameter studied that shows no variability. The posterior medial resection and the tibial slope are not included in the variability analysis, as these values are predetermined by the standardized protocol. Discussion Our study is the first in vivo study to demonstrate good to excellent inter- and intra-observer reliability of the ROSA® system for laxity assessment and planning between high- and low-volume surgeons. To our knowledge, no in vivo studies have examined the reproducibility of knee laxity assessments using the ROSA® robotic system across surgeons with varying experience levels. However, soft tissue laxity is known to exhibit high variability and limited predictability based on preoperative deformities. Therefore, performing a soft tissue assessment before bone resections and tissue releases is suggested to improve both measured-resection and gap-balancing techniques [21]. Focusing on the ROSA® robotic system, Charette et al. have demonstrated in a cadaveric study its excellent inter- and intra-rater reliability in registering anatomical bony landmarks and performing dynamic assessments [22]. Few cadaveric studies have explored the accuracy and reproducibility of this system [23, 24], while many in vivo studies focus on achieving precise bone cuts, planned angles, and resections [7, 10, 25–27]. Among all the parameters that have been studied, most are demonstrated to have excellent to good reproducibility. Nonetheless, of the 26 parameters analyzed (Tables 2 and 3), only three show poor ICC values during the planning step: stylus height, femoral implant flexion, and the medial distal femoral cut (Table 3). The results for stylus height and femoral implant could be explained by the resident who strictly follows the ligament balance algorithm, aligning numerical values within the software to minimize the risk of anterior cortical notching. This reflects a cautious approach by the novice surgeon, whereas experienced surgeons do not consider minor variations of one degree or 0.5 mm to significantly impact final prosthesis positioning. The variability observed in the medial distal femoral cut can be attributed to differences in surgical experience. Senior surgeons tend to tolerate a tighter or more constrained medial femorotibial compartment. According to the balance algorithm protocol, after adjustments to tibial varus/valgus, implant rotation, and femoral varus/valgus, modifying the distal femoral cut height is the only remaining option to balance the knee at the final stage of planning. Such differences can be explained by the small sample size studied, as even slight changes can quickly affect variability. This preference among senior surgeons can be explained by their perception that a larger-than-expected medial femorotibial space is often obtained after bone resections. Tibial and femoral resections may lead to collateral ligament and joint capsule loosening, introducing discrepancies with the initial robotic assessment. Additionally, posterior femoral osteophytes, often not completely removed before osteotomy, can influence gap dynamics by eliminating the tenting effect [28]. The analysis of these data between surgeons with different experience levels is conducted based on a single registering of anatomical bony landmarks. Therefore, the impact of variability in landmark acquisition on laxity assessment, bone cut planning, and implant positioning is not assessed in this study. Nonetheless, caution is warranted, as some authors have demonstrated that variations in landmark acquisition can alter the robotic reference frame, potentially leading to inaccuracies and discrepancies between preoperative planning and final surgical outcomes [29, 30] Some authors have suggested a likely inverse volume–outcome relationship for TKA outcomes, including mortality, readmissions, and potentially early revision rates [31]. These findings may reflect the superior knee assessment skills of experienced surgeons, as well as their ability to set and achieve realistic operative goals. Their experience and subjective assessments constitute a strongly present decision-making element, ensuring the execution of their positioning philosophies. With the emergence of new technologies improving the precision and reproducibility of knee assessments, a reduction in these differences could be expected. Limitation This study has several limitations. It is conducted at a single center with a relatively small sample size and a predominantly female cohort. Additionally, the higher proportion of varus knees compared to valgus knees limits the applicability of the findings to valgus cases. Furthermore, the results are interpretable only for the ROSA® system, as each TKA robotic platform requires a specific analysis due to its unique acquisition process and bone-cutting methods [29]. Conclusion In conclusion, our in vivo findings confirm that the ROSA® robotic system demonstrates good-to-excellent reproducibility between low- and high-volume surgeons in measuring soft tissue laxity and gaps under manual stress, as well as in planning bone cuts and implant positioning according to a standardized protocol. Only minor differences are observed during the planning step, specifically in stylus height, femoral implant flexion, and particularly in the distal femoral cut, which shows greater variability, likely reflecting the experienced surgeon's tendency to adjust the plan based on personal judgment. Declarations Funding Declaration : The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Author Contribution All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by E.J. and B.B. The first draft of the manuscript was written by E.J. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. References Gunaratne R, Pratt DN, Banda J, et al (2017) Patient Dissatisfaction Following Total Knee Arthroplasty: A Systematic Review of the Literature. The Journal of Arthroplasty 32:3854–3860. https://doi.org/10.1016/j.arth.2017.07.021 Nam D, Nunley RM, Barrack RL (2014) Patient dissatisfaction following total knee replacement: a growing concern? The Bone & Joint Journal 96-B:96–100. https://doi.org/10.1302/0301-620X.96B11.34152 Kayani B, Konan S, Ayuob A, et al (2019) Robotic technology in total knee arthroplasty: a systematic review. EFORT Open Rev 4:611–617. https://doi.org/10.1302/2058-5241.4.190022 Rossi SMP, Benazzo F (2023) Individualized alignment and ligament balancing technique with the ROSA® robotic system for total knee arthroplasty. International Orthopaedics (SICOT) 47:755–762. https://doi.org/10.1007/s00264-022-05671-z Wininger AE, Lambert BS, Sullivan TC, et al (2023) Robotic-Assisted Total Knee Arthroplasty Can Increase Frequency of Achieving Target Limb Alignment in Primary Total Knee Arthroplasty for Preoperative Valgus Deformity. Arthroplasty Today 23:101196. https://doi.org/10.1016/j.artd.2023.101196 Alrajeb R, Zarti M, Shuia Z, et al (2024) Robotic-assisted versus conventional total knee arthroplasty: a systematic review and meta-analysis of randomized controlled trials. Eur J Orthop Surg Traumatol 34:1333–1343. https://doi.org/10.1007/s00590-023-03798-2 Gamie Z, Kenanidis E, Douvlis G, et al (2024) Accuracy of the Imageless Mode of the ROSA Robotic System for Targeted Resection Thickness in Total Knee Arthroplasty: A Prospective, Single Surgeon Case‐Series Study. Robotics Computer Surgery 20:e70029. https://doi.org/10.1002/rcs.70029 Riantho A, Butarbutar JCP, Fidiasrianto K, et al (2023) Radiographic Outcomes of Robot-Assisted Versus Conventional Total Knee Arthroplasty. JB JS Open Access 8:e23.00010. https://doi.org/10.2106/JBJS.OA.23.00010 Ruangsomboon P, Ruangsomboon O, Pornrattanamaneewong C, et al (2023) Clinical and radiological outcomes of robotic-assisted versus conventional total knee arthroplasty: a systematic review and meta-analysis of randomized controlled trials. Acta Orthop 94:60–79. https://doi.org/10.2340/17453674.2023.9411 Zaidi F, Goplen CM, Bolam SM, Monk AP (2024) Accuracy and Outcomes of a Novel Cut-Block Positioning Robotic-Arm Assisted System for Total Knee Arthroplasty: A Systematic Review and Meta-Analysis. Arthroplasty Today 29:101451. https://doi.org/10.1016/j.artd.2024.101451 Ma N, Sun P, Xin P, et al (2024) Comparison of the efficacy and safety of MAKO robot-assisted total knee arthroplasty versus conventional manual total knee arthroplasty in uncomplicated unilateral total knee arthroplasty a single-centre retrospective analysis. International Orthopaedics (SICOT) 48:2351–2358. https://doi.org/10.1007/s00264-024-06234-0 Smith AF, Eccles CJ, Bhimani SJ, et al (2021) Improved Patient Satisfaction following Robotic-Assisted Total Knee Arthroplasty. J Knee Surg 34:730–738. https://doi.org/10.1055/s-0039-1700837 Shatrov J, Parker D (2020) Computer and robotic – assisted total knee arthroplasty: a review of outcomes. J Exp Orthop 7:70. https://doi.org/10.1186/s40634-020-00278-y Kayani B, Fontalis A, Haddad IC, et al (2023) Robotic-arm assisted total knee arthroplasty is associated with comparable functional outcomes but improved forgotten joint scores compared with conventional manual total knee arthroplasty at five-year follow-up. Knee Surgery, Sports Traumatology, Arthroscopy 31:5453–5462. https://doi.org/10.1007/s00167-023-07578-7 Kenanidis E, Paparoidamis G, Milonakis N, et al (2023) Comparative outcomes between a new robotically assisted and a manual technique for total knee arthroplasty in patients with osteoarthritis: a prospective matched comparative cohort study. Eur J Orthop Surg Traumatol 33:1231–1236. https://doi.org/10.1007/s00590-022-03274-3 Kort N, Stirling P, Pilot P, Müller JH (2022) Robot-assisted knee arthroplasty improves component positioning and alignment, but results are inconclusive on whether it improves clinical scores or reduces complications and revisions: a systematic overview of meta-analyses. Knee Surgery, Sports Traumatology, Arthroscopy 30:1795. https://doi.org/10.1007/s00167-021-06472-4 Hoveidaei AH, Esmaeili S, Ghaseminejad-Raeini A, et al (2024) Robotic assisted Total Knee Arthroplasty (TKA) is not associated with increased patient satisfaction: a systematic review and meta-analysis. International Orthopaedics (SICOT) 48:1771–1784. https://doi.org/10.1007/s00264-024-06206-4 Lan Y-T, Chen Y-W, Niu R, et al (2023) The trend and future projection of technology-assisted total knee arthroplasty in the United States. The International Journal of Medical Robotics and Computer Assisted Surgery 19:e2478. https://doi.org/10.1002/rcs.2478 Vanlommel L, Neven E, Anderson MB, et al (2021) The initial learning curve for the ROSA® Knee System can be achieved in 6-11 cases for operative time and has similar 90-day complication rates with improved implant alignment compared to manual instrumentation in total knee arthroplasty. J Exp Orthop 8:119. https://doi.org/10.1186/s40634-021-00438-8 Shah SM (2021) After 25 years of computer-navigated total knee arthroplasty, where do we stand today? Arthroplasty 3:41. https://doi.org/10.1186/s42836-021-00100-9 Yee DKH, Leung JTC, Chu V, et al (2024) Reliability of pre-resection ligament tension assessment in imageless robotic assisted total knee replacement. Arthroplasty 6:44. https://doi.org/10.1186/s42836-024-00266-y Charette RS, Sarpong NO, Weiner TR, et al (2022) Registration of Bony Landmarks and Soft Tissue Laxity during Robotic Total Knee Arthroplasty is Highly Reproducible. Surg Technol Int 41:sti41/1633. https://doi.org/10.52198/22.STI.41.OS1633 Seidenstein A, Birmingham M, Foran J, Ogden S (2021) Better accuracy and reproducibility of a new robotically-assisted system for total knee arthroplasty compared to conventional instrumentation: a cadaveric study. Knee Surg Sports Traumatol Arthrosc 29:859–866. https://doi.org/10.1007/s00167-020-06038-w Parratte S, Price AJ, Jeys LM, et al (2019) Accuracy of a New Robotically Assisted Technique for Total Knee Arthroplasty: A Cadaveric Study. J Arthroplasty 34:2799–2803. https://doi.org/10.1016/j.arth.2019.06.040 Alessi A, Fitzcharles E, Weber IC, Cafferky NL (2021) The Functionality of a Novel Robotic Surgical Assistant for Total Knee Arthroplasty: A Case Series. Case Reports in Orthopedics 2021:6659707. https://doi.org/10.1155/2021/6659707 Mayne AI, Rajgor H, Munasinghe C, et al (2024) The ROSA robotic-arm system reliably restores joint line height, patella height and posterior condylar offset in total knee arthroplasty. The Knee 48:1–7. https://doi.org/10.1016/j.knee.2024.02.007 Wininger AE, Lambert BS, Sullivan TC, et al (2023) Robotic-Assisted Total Knee Arthroplasty Can Increase Frequency of Achieving Target Limb Alignment in Primary Total Knee Arthroplasty for Preoperative Valgus Deformity. Arthroplast Today 23:101196. https://doi.org/10.1016/j.artd.2023.101196 Lee JH, Jung HJ, Lee JK, et al (2023) Large Osteophytes over 10 mm at Posterior Medial Femoral Condyle Can Lead to Asymmetric Extension Gap Following Bony Resection in Robotic Arm–Assisted Total Knee Arthroplasty with Pre-Resection Gap Balancing. JCM 12:5980. https://doi.org/10.3390/jcm12185980 Shin C, Crovetti C, Huo E, Lionberger D (2022) Unsatisfactory accuracy of recent robotic assisting system ROSA for total knee arthroplasty. J Exp Orthop 9:82. https://doi.org/10.1186/s40634-022-00522-7 Eggermont E, Janssens R, Ulrix M, et al (2025) Sagittal accuracy and functional impact of tibial slope in imageless robotic-assisted Total Knee Arthroplasty. International Orthopaedics (SICOT). https://doi.org/10.1007/s00264-025-06472-w Liddle AD, Pandit H, Judge A, Murray DW (2016) Effect of Surgical Caseload on Revision Rate Following Total and Unicompartmental Knee Replacement: The Journal of Bone and Joint Surgery 98:1–8. https://doi.org/10.2106/JBJS.N.00487 Tables Table 1 Patient demographics. Characteristics Values Gender (male:female) 3:14 Left:right 7:10 Mean ± SD Age (years) 53.47 ± 8.30 Mean HKA angle (°) 3.68 ± 4.24 Varus:Valgus 14:2 A positive value denotes varus alignment; SD: standard deviation; HKA: hip–knee–ankle. Table 2 Intra- and inter-class correlation coefficients (ICC) results for HKA and coronal laxity measurements during the acquisition step. Variable Inter-rater agreement (95% CI) Intra-rater reliability (95% CI) HKA (°) 0.979 (0.943 – 0.992) 0.985 (0.957 – 0.995) Extension (0°) Maximum Extension (°) 0.938 (0.327 – 0.985) 0.987 (0.964 – 0.995) Medial Laxity (mm) 0.937 (0.836 – 0.977) 0.781 (0.499 – 0.914) Lateral Laxity (mm) 0.887 (0.703 – 0.958) 0.857 (0.648 – 0.946) Varus Stress (°) 0.973 (0.927 – 0.99) 0.982 (0.951 – 0.993) Valgus Stress (°) 0.913 (0.662 – 0.972) 0.919 (0.791 – 0.970) Laxity profile: Absolute (°) 0.624 (0.202 – 0.847) 0.729 (0.392 – 0.893) Flexion (90°) Maximum Flexion (°) 0.937 (0.836 – 0.977) 0.781 (0.499 – 0.914) Medial Laxity (mm) 0.879 (0.696 – 0.954) 0.949 (0.866 – 0.981) Lateral Laxity (mm) 0.786 (0.498 - 0.917) 0.824 (0.588 – 0.932) Varus Stress (°) 0.876 (0.689 – 0.953) 0.960 (0.894 – 0.985) Valgus Stress (°) 0.895 (0.708 – 0.962) 0.882 (0.642 – 0.959) Laxity profile: Absolute (°) 0.653 (0.279 – 0.858) 0.855 (0.614 – 0.947) The ICC values indicate excellent agreement (>0.75) and good agreement (>0.60). Laxity profile: total range of laxity, expressed in absolute values, calculated based on the extreme values obtained during varus and valgus assessments. Table 3 Intra- and inter-class correlation coefficients (ICC) results for the positioning of implant components, final HKA, and implant size estimation during the planning step. Variable Inter-rater agreement (95% CI) Intra-rater reliability (95% CI) Femur Flexion (°) 0.211 (-0.317 – 0.626) 0.874 (0.692 – 0.952) Stylus height 0.482 (0.024 – 0.775) 0.707 (0.352 – 0.884) Distal Varus/valgus (°) 0.763 (0.453 – 0.908) 0.85 (0.633 – 0.943) Distal medial resection (mm) 0.521 (0.074 – 0.795) 0.696 (0.335 – 0.878) Distal lateral resection (mm) 0.803 (0.544 – 0.923) 0.924 (0.799 – 0.972) Posterior medial resection (mm) / / Posterior lateral resection (mm) 0.755 (0.44 – 0.904) 0.975 (0.934 – 0.991) Rotation from PCA (°) 0.825 (0.577 – 0.933) 0.948 (0.864 – 0.981) Tibia Slope (°) / / Proximal varus/valgus (°) 0.87 (0.682 – 0.95) 0.896 (0.704 – 0.963) Proximal medial resection (mm) 0.786 (0.509 – 0.916) 0.924 (0.791 – 0.973) Proximal lateral resection (mm) 0.875 (0.577 – 0.933) 0.937 (0.741 – 0.98) Final planification HKA (°) 0.751 (0.436 – 0.902) 0.651 (0.279 – 0.856) FEMUR - Implant size 0.969 (0.918 – 0.989) 1 PE - Implant size 1 1 The ICC values indicate excellent agreement (>0.75) and good agreement (>0.60). Additional Declarations No competing interests reported. Supplementary Files Annex1.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 12 Sep, 2025 Reviewers invited by journal 29 Mar, 2025 Editor assigned by journal 28 Mar, 2025 Submission checks completed at journal 28 Mar, 2025 First submitted to journal 18 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6252951","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":439851466,"identity":"74709ea2-7034-4e22-9e6a-3d986acb7421","order_by":0,"name":"Johnatan 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George","correspondingAuthor":false,"prefix":"","firstName":"Arnaud","middleName":"","lastName":"Clavé","suffix":""},{"id":439851482,"identity":"3f00014b-9284-4cf0-b504-6dd58c89c774","order_by":8,"name":"Jacques Hernigou","email":"","orcid":"","institution":"Epicura","correspondingAuthor":false,"prefix":"","firstName":"Jacques","middleName":"","lastName":"Hernigou","suffix":""},{"id":439851483,"identity":"339cf58e-ed04-41c1-bcbc-4b1d19a8f27c","order_by":9,"name":"Bruno Baillon","email":"","orcid":"","institution":"Hôpitaux Iris Sud","correspondingAuthor":false,"prefix":"","firstName":"Bruno","middleName":"","lastName":"Baillon","suffix":""}],"badges":[],"createdAt":"2025-03-18 12:08:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6252951/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6252951/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81013229,"identity":"c48024dd-d341-4d2c-873d-a3ce9fb27ccd","added_by":"auto","created_at":"2025-04-21 08:34:36","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":66222,"visible":true,"origin":"","legend":"\u003cp\u003eInitial evaluation of range of motion (ROM) in degrees and coronal laxity in varus/valgus stress in degrees (1) with femoro-tibial spaces in millimeters for flexion and extension (2)\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6252951/v1/f48bfc85dbb86d5ebccdeb51.jpg"},{"id":81014609,"identity":"f5755566-985c-4214-8921-7cecbbe39682","added_by":"auto","created_at":"2025-04-21 08:42:36","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":107875,"visible":true,"origin":"","legend":"\u003cp\u003ePlanning with estimated bone cuts in flexion and extension in millimeters (3) and proposed final implant size (4).\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6252951/v1/e1ec5e85f34ee4a384ea0e47.jpg"},{"id":81015392,"identity":"2f9036da-66c9-46c1-9aba-4b696ccb51ae","added_by":"auto","created_at":"2025-04-21 08:50:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":749334,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6252951/v1/bbc2f938-273f-454f-afbe-5c1982f9185d.pdf"},{"id":81013233,"identity":"9b093afe-694d-4429-b617-fc918da65235","added_by":"auto","created_at":"2025-04-21 08:34:36","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16261,"visible":true,"origin":"","legend":"","description":"","filename":"Annex1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6252951/v1/7ea1a0b804c9ba62f73f68b0.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eInter- and intra-operator variability in ligament balance measurements in total knee arthroplasty with the robotic navigation system (ROSA®): in vivo study\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDespite improvements in prosthetic designs and alignment techniques, up to 20% of patients still report knee pain or are unsatisfied with TKA [1, 2], leading sometimes to revision arthroplasty. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdvances in technology seek to tackle these challenges, but the value of these innovations is still a topic of controversy. The introduction of robotic-assisted total knee arthroplasty (RA-TKA) aims to enhance the accuracy of surgical bone resection and alignment, optimize component positioning, minimize soft-tissue damage, enable ligament balancing of the knee (for surgeons who incorporate this approach into their surgical philosophy), and facilitate the tailored placement of standard off-the-shelf implants according to the patient’s specific parameters [3–5].\u003c/p\u003e\n\u003cp\u003eHowever, while some studies confirm that robotic assistance has indeed led to more accurate component positioning [4, 6–11], other studies indicate that its effects on clinical outcomes, patient satisfaction, and long-term survivorship\u0026nbsp;of implants remain to be fully\u0026nbsp;validated [6, 9, 12–17].\u003c/p\u003e\n\u003cp\u003eThe increasing use of robotic systems in TKA [18], is also driven by their ability to improve the reproducibility of surgeries, allowing surgeons of varying experience levels to achieve alignment goals with accuracy and precision comparable to more established surgeons [5], while offering a relatively short learning curve. [19]\u003c/p\u003e\n\u003cp\u003eThe robot is therefore used as a precision tool, but its accuracy depends on two main factors: the intrinsic precision of the robot itself and the quality of data acquired by the surgeon. To achieve bone resection, implant size, and implant positioning, the intraoperative assessment of bony landmarks and clinically significant parameters, like the soft tissue balancing in flexion and extension, are critical. This step is one of the most important in the operating room, as all following surgical actions are based on this information.\u003c/p\u003e\n\u003cp\u003eThus, if any of this information is incorrect, it will result in the well-known \"garbage in, garbage out\" effect [20], resulting in poor planning and consequently wrong positioning of the implants (garbage out). It is therefore crucial that the user's proficiency with the robot minimally affects the quality of the acquisitions (garbage in). Given the importance of accurate intraoperative measurements, it is essential to assess how variability in surgeon experience affects the precision of the system.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study aims to evaluate intra- and inter-operator variability according to surgeon experience during intraoperative gap assessment and bone-cut planning using the imageless ROSA® robotic system.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors hypothesize that good inter- and intra-operator variability for both surgical phases will be observed, regardless of surgeon experience.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the local Ethics Committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBetween July and September 2023, seventeen robotic-assisted total knee arthroplasty (RA-TKA) was performed by the senior surgeon in collaboration with the same resident in a single-center prospective study. The posterior stabilized (PS) Persona® implant was placed with the assistance of the semi-active robotic system ROSA® (Software and hardware by Zimmer Biomet, Warsaw, IN).\u003c/p\u003e\n\u003cp\u003eSurgical indications consisted of symptomatic tricompartmental knee osteoarthritis in adult patients (\u0026gt;18 years), with no history of previous knee surgery. Contraindications were defined as post-traumatic pathology or inflammatory arthritis, revision arthroplasty, as well as neurological or muscular pathologies affecting the lower limbs. The severity of osteoarthritis and/or axial deviation did not influence patient selection for the study. Patients adhered to the institution's standard care pathway until the day of surgery.\u003c/p\u003e\n\u003cp\u003eData were collected only when the resident assisted the compared senior surgeon. The ROSA® software allowed data to be exported in the form of an Excel file, while screenshots were included in \".jpeg\" format to document the planning process. Data were associated with the patient through an alphanumeric code created by the Zimmer-Biomet database software before the surgery.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSurgical Step\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe operation began with the classic setup of the ROSA® robotic system and surgical parapatellar knee approach. Once the knee was exposed and osteophytes were removed, navigation trackers were conventionally placed by the senior surgeon on the femur and tibia. Only the imageless mode was used on the robotic system, which meant that planning was done intraoperatively based on the acquisition.\u003c/p\u003e\n\u003cp\u003eAll bony landmarks necessary to map the joint were collected, which determined the measurement of the native knee coronal alignment with Hip-Knee-Ankle (HKA) angle, range of motion, and ligament balancing, all of which were recorded by the software.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasurements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcquisition\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLigament balance measurements were performed alternately by the senior surgeon and the resident. All measurements were conducted following the same routine protocol. Measurements were obtained through manual stress in valgus and varus of the knee (Figure 1) at 0° and 90° of flexion, defining joint gaps (Figure 2).\u003c/p\u003e\n\u003cp\u003eThree evaluations were required in total to assess inter- and intra-operator variability. Inter-operator variability was evaluated between the senior surgeon and the resident, while intra-operator variability was assessed with a second measurement by the senior.\u003c/p\u003e\n\u003cp\u003eTo avoid confirmation bias, specific measures were implemented throughout the evaluation process. Initially, during the first assessment (inter-operator), the senior surgeon conducted the evaluation without providing visual access to the monitor to the resident. Similarly, during the third evaluation (intra-operator), the senior surgeon abstained from having visual access to the monitor. These steps were taken to ensure impartiality and minimize the potential influence of preconceived notions on the evaluation outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePlanification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn line with the philosophy of the similar ligament balancing protocol adopted by the operator, the sequence of modification of different parameters followed the same steps (Annex 1). Measurements of bone cuts as well as the sizing of the femoral implant and polyethylene (Figure 2) were recorded at the end of each laxity assessment and planning. The size of the tibial implant was not retained due to default parameters in the software, which suggested a default size type in imageless procedures.\u003c/p\u003e\n\u003cp\u003eFinal bone cuts were performed based on the measurements from the senior surgeon's last ligament balance and planning, which did not alter the initial patient care. No additional or exceptional interventions were performed. Indeed, during acquisition, measurements were repeatedly reassessed as part of the routine, which did not substantially increase operative time and therefore did not pose an increased risk to the patient.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInter-rater and intra-rater reliabilities, using two-way random intra- and inter-class correlation coefficients (ICCs), were calculated between all measured variables in order to test the reproducibility and repeatability of the proposed technique. The ICC (calculated using k-Rating, Absolute-Agreement, 2-Way Random-Effects Model) is by definition measured in a range from 0 to 1, with a value close to 1 indicating better agreement: an ICC \u0026gt; 0.75 indicates excellent agreement, while an ICC \u0026gt; 0.60 is taken as a threshold for a good agreement. The R software (R Core Team, 2021), version 4.2.0. was used to produce the results.\u0026nbsp;\u003c/p\u003e"},{"header":"Results ","content":"\u003cp\u003eSeventeen patients were recruited for this study (3 males and 14 females). The average age was 53.47 \u0026plusmn; 8.30 years. Of the 17 knees evaluated, 7 were on the left side and 10 on the right, with 14 classified as varus and 2 as valgus (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcquisition\u0026nbsp;\u003c/strong\u003eThe ICCs resulting from the acquisition step, including the Hip-Knee-Ankle (HKA) angle, range of motion (ROM), and ligament balancing (coronal laxity and flexion/extension spaces), are reported in Table 2. All ICC values indicate excellent agreement (\u0026gt;0.75), demonstrating high reproducibility, with consistent results observed for both the senior surgeon (average intra-observer variability \u0026lt; 1\u0026deg; for all flexion angles) and the resident (average inter-observer variability \u0026lt; 1\u0026deg; for all flexion angles), except for the laxity profile. The ICC values of the laxity profile (total range of laxity, expressed in absolute values, based on the extreme values obtained during varus and valgus manual stress) show good inter-rater agreement (\u0026gt;0.60) in flexion and extension and good intra-rater agreement (\u0026gt;0.60) in extension only.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePlanification\u003c/strong\u003e The ICC values from the planning step, including the estimation of bone resections, implant positioning in varus/valgus, and adjustments for femoral component flexion and rotation relative to the posterior condylar axis (PCA), indicate good (\u0026gt;0.60) to excellent (\u0026gt;0.75) agreement, except for the stylus height, femoral implant flexion, and medial distal femoral cut (Table 3). The polyethylene size is the only parameter studied that shows no variability. The posterior medial resection and the tibial slope are not included in the variability analysis, as these values are predetermined by the standardized protocol.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study is the first in vivo study to demonstrate good to excellent inter- and intra-observer reliability of the ROSA® system for laxity assessment and planning between high- and low-volume surgeons. To our knowledge, no in vivo studies have examined the reproducibility of knee laxity assessments using the ROSA® robotic system across surgeons with varying experience levels. However, soft tissue laxity is known to exhibit high variability and limited predictability based on preoperative deformities. Therefore, performing a soft tissue assessment before bone resections and tissue releases is suggested to improve both measured-resection and gap-balancing techniques [21]. Focusing on the ROSA® robotic system, Charette et al. have demonstrated in a cadaveric study its excellent inter- and intra-rater reliability in registering anatomical bony landmarks and performing dynamic assessments [22]. Few cadaveric studies have explored the accuracy and reproducibility of this system [23, 24], while many in vivo studies focus on achieving precise bone cuts, planned angles, and resections [7, 10, 25–27].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAmong all the parameters that have been studied, most are demonstrated to have excellent to good reproducibility. Nonetheless, of the 26 parameters analyzed (Tables 2 and 3), only three show poor ICC values during the planning step: stylus height, femoral implant flexion, and the medial distal femoral cut (Table 3). The results for stylus height and femoral implant could be explained by the resident who strictly follows the ligament balance algorithm, aligning numerical values within the software to minimize the risk of anterior cortical notching. This reflects a cautious approach by the novice surgeon, whereas experienced surgeons do not consider minor variations of one degree or 0.5 mm to significantly impact final prosthesis positioning.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe variability observed in the medial distal femoral cut can be attributed to differences in surgical experience. Senior surgeons tend to tolerate a tighter or more constrained medial femorotibial compartment. According to the balance algorithm protocol, after adjustments to tibial varus/valgus, implant rotation, and femoral varus/valgus, modifying the distal femoral cut height is the only remaining option to balance the knee at the final stage of planning. Such differences can be explained by the small sample size studied, as even slight changes can quickly affect variability. This preference among senior surgeons can be explained by their perception that a larger-than-expected medial femorotibial space is often obtained after bone resections. Tibial and femoral resections may lead to collateral ligament and joint capsule loosening, introducing discrepancies with the initial robotic assessment. Additionally, posterior femoral osteophytes, often not completely removed before osteotomy, can influence gap dynamics by eliminating the tenting effect\u0026nbsp;[28].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe analysis of these data between surgeons with different experience levels is conducted based on a single registering of anatomical bony landmarks. Therefore, the impact of variability in landmark acquisition on laxity assessment, bone cut planning, and implant positioning is not assessed in this study. Nonetheless, caution is warranted, as some authors have demonstrated that variations in landmark acquisition can alter the robotic reference frame, potentially leading to inaccuracies and discrepancies between preoperative planning and final surgical outcomes\u0026nbsp;[29, 30]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSome authors have suggested a likely inverse volume–outcome relationship for TKA outcomes, including mortality, readmissions, and potentially early revision rates\u0026nbsp;[31]. These findings may reflect the superior knee assessment skills of experienced surgeons, as well as their ability to set and achieve realistic operative goals. Their experience and subjective assessments constitute a strongly present decision-making element, ensuring the execution of their positioning philosophies. With the emergence of new technologies improving the precision and reproducibility of knee assessments, a reduction in these differences could be expected.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has several limitations. It is conducted at a single center with a relatively small sample size and a predominantly female cohort. Additionally, the higher proportion of varus knees compared to valgus knees limits the applicability of the findings to valgus cases. Furthermore, the results are interpretable only for the ROSA® system, as each TKA robotic platform requires a specific analysis due to its unique acquisition process and bone-cutting methods [29].\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our in vivo findings confirm that the ROSA\u0026reg; robotic system demonstrates good-to-excellent reproducibility between low- and high-volume surgeons in measuring soft tissue laxity and gaps under manual stress, as well as in planning bone cuts and implant positioning according to a standardized protocol. Only minor differences are observed during the planning step, specifically in stylus height, femoral implant flexion, and particularly in the distal femoral cut, which shows greater variability, likely reflecting the experienced surgeon\u0026apos;s tendency to adjust the plan based on personal judgment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cu\u003eFunding Declaration :\u003c/u\u003e\u003c/strong\u003e The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u0026nbsp;\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by E.J. and B.B. The first draft of the manuscript was written by E.J. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGunaratne R, Pratt DN, Banda J, et al (2017) Patient Dissatisfaction Following Total Knee Arthroplasty: A Systematic Review of the Literature. The Journal of Arthroplasty 32:3854\u0026ndash;3860. https://doi.org/10.1016/j.arth.2017.07.021\u003c/li\u003e\n\u003cli\u003eNam D, Nunley RM, Barrack RL (2014) Patient dissatisfaction following total knee replacement: a growing concern? The Bone \u0026amp; Joint Journal 96-B:96\u0026ndash;100. https://doi.org/10.1302/0301-620X.96B11.34152\u003c/li\u003e\n\u003cli\u003eKayani B, Konan S, Ayuob A, et al (2019) Robotic technology in total knee arthroplasty: a systematic review. EFORT Open Rev 4:611\u0026ndash;617. https://doi.org/10.1302/2058-5241.4.190022\u003c/li\u003e\n\u003cli\u003eRossi SMP, Benazzo F (2023) Individualized alignment and ligament balancing technique with the ROSA\u0026reg; robotic system for total knee arthroplasty. International Orthopaedics (SICOT) 47:755\u0026ndash;762. https://doi.org/10.1007/s00264-022-05671-z\u003c/li\u003e\n\u003cli\u003eWininger AE, Lambert BS, Sullivan TC, et al (2023) Robotic-Assisted Total Knee Arthroplasty Can Increase Frequency of Achieving Target Limb Alignment in Primary Total Knee Arthroplasty for Preoperative Valgus Deformity. Arthroplasty Today 23:101196. https://doi.org/10.1016/j.artd.2023.101196\u003c/li\u003e\n\u003cli\u003eAlrajeb R, Zarti M, Shuia Z, et al (2024) Robotic-assisted versus conventional total knee arthroplasty: a systematic review and meta-analysis of randomized controlled trials. Eur J Orthop Surg Traumatol 34:1333\u0026ndash;1343. https://doi.org/10.1007/s00590-023-03798-2\u003c/li\u003e\n\u003cli\u003eGamie Z, Kenanidis E, Douvlis G, et al (2024) Accuracy of the Imageless Mode of the ROSA Robotic System for Targeted Resection Thickness in Total Knee Arthroplasty: A Prospective, Single Surgeon Case‐Series Study. Robotics Computer Surgery 20:e70029. https://doi.org/10.1002/rcs.70029\u003c/li\u003e\n\u003cli\u003eRiantho A, Butarbutar JCP, Fidiasrianto K, et al (2023) Radiographic Outcomes of Robot-Assisted Versus Conventional Total Knee Arthroplasty. JB JS Open Access 8:e23.00010. https://doi.org/10.2106/JBJS.OA.23.00010\u003c/li\u003e\n\u003cli\u003eRuangsomboon P, Ruangsomboon O, Pornrattanamaneewong C, et al (2023) Clinical and radiological outcomes of robotic-assisted versus conventional total knee arthroplasty: a systematic review and meta-analysis of randomized controlled trials. Acta Orthop 94:60\u0026ndash;79. https://doi.org/10.2340/17453674.2023.9411\u003c/li\u003e\n\u003cli\u003eZaidi F, Goplen CM, Bolam SM, Monk AP (2024) Accuracy and Outcomes of a Novel Cut-Block Positioning Robotic-Arm Assisted System for Total Knee Arthroplasty: A Systematic Review and Meta-Analysis. Arthroplasty Today 29:101451. https://doi.org/10.1016/j.artd.2024.101451\u003c/li\u003e\n\u003cli\u003eMa N, Sun P, Xin P, et al (2024) Comparison of the efficacy and safety of MAKO robot-assisted total knee arthroplasty versus conventional manual total knee arthroplasty in uncomplicated unilateral total knee arthroplasty a single-centre retrospective analysis. International Orthopaedics (SICOT) 48:2351\u0026ndash;2358. https://doi.org/10.1007/s00264-024-06234-0\u003c/li\u003e\n\u003cli\u003eSmith AF, Eccles CJ, Bhimani SJ, et al (2021) Improved Patient Satisfaction following Robotic-Assisted Total Knee Arthroplasty. J Knee Surg 34:730\u0026ndash;738. https://doi.org/10.1055/s-0039-1700837\u003c/li\u003e\n\u003cli\u003eShatrov J, Parker D (2020) Computer and robotic \u0026ndash; assisted total knee arthroplasty: a review of outcomes. J Exp Orthop 7:70. https://doi.org/10.1186/s40634-020-00278-y\u003c/li\u003e\n\u003cli\u003eKayani B, Fontalis A, Haddad IC, et al (2023) Robotic-arm assisted total knee arthroplasty is associated with comparable functional outcomes but improved forgotten joint scores compared with conventional manual total knee arthroplasty at five-year follow-up. Knee Surgery, Sports Traumatology, Arthroscopy 31:5453\u0026ndash;5462. https://doi.org/10.1007/s00167-023-07578-7\u003c/li\u003e\n\u003cli\u003eKenanidis E, Paparoidamis G, Milonakis N, et al (2023) Comparative outcomes between a new robotically assisted and a manual technique for total knee arthroplasty in patients with osteoarthritis: a prospective matched comparative cohort study. Eur J Orthop Surg Traumatol 33:1231\u0026ndash;1236. https://doi.org/10.1007/s00590-022-03274-3\u003c/li\u003e\n\u003cli\u003eKort N, Stirling P, Pilot P, M\u0026uuml;ller JH (2022) Robot-assisted knee arthroplasty improves component positioning and alignment, but results are inconclusive on whether it improves clinical scores or reduces complications and revisions: a systematic overview of meta-analyses. Knee Surgery, Sports Traumatology, Arthroscopy 30:1795. https://doi.org/10.1007/s00167-021-06472-4\u003c/li\u003e\n\u003cli\u003eHoveidaei AH, Esmaeili S, Ghaseminejad-Raeini A, et al (2024) Robotic assisted Total Knee Arthroplasty (TKA) is not associated with increased patient satisfaction: a systematic review and meta-analysis. International Orthopaedics (SICOT) 48:1771\u0026ndash;1784. https://doi.org/10.1007/s00264-024-06206-4\u003c/li\u003e\n\u003cli\u003eLan Y-T, Chen Y-W, Niu R, et al (2023) The trend and future projection of technology-assisted total knee arthroplasty in the United States. The International Journal of Medical Robotics and Computer Assisted Surgery 19:e2478. https://doi.org/10.1002/rcs.2478\u003c/li\u003e\n\u003cli\u003eVanlommel L, Neven E, Anderson MB, et al (2021) The initial learning curve for the ROSA\u0026reg; Knee System can be achieved in 6-11 cases for operative time and has similar 90-day complication rates with improved implant alignment compared to manual instrumentation in total knee arthroplasty. J Exp Orthop 8:119. https://doi.org/10.1186/s40634-021-00438-8\u003c/li\u003e\n\u003cli\u003eShah SM (2021) After 25\u0026thinsp;years of computer-navigated total knee arthroplasty, where do we stand today? Arthroplasty 3:41. https://doi.org/10.1186/s42836-021-00100-9\u003c/li\u003e\n\u003cli\u003eYee DKH, Leung JTC, Chu V, et al (2024) Reliability of pre-resection ligament tension assessment in imageless robotic assisted total knee replacement. Arthroplasty 6:44. https://doi.org/10.1186/s42836-024-00266-y\u003c/li\u003e\n\u003cli\u003eCharette RS, Sarpong NO, Weiner TR, et al (2022) Registration of Bony Landmarks and Soft Tissue Laxity during Robotic Total Knee Arthroplasty is Highly Reproducible. Surg Technol Int 41:sti41/1633. https://doi.org/10.52198/22.STI.41.OS1633\u003c/li\u003e\n\u003cli\u003eSeidenstein A, Birmingham M, Foran J, Ogden S (2021) Better accuracy and reproducibility of a new robotically-assisted system for total knee arthroplasty compared to conventional instrumentation: a cadaveric study. Knee Surg Sports Traumatol Arthrosc 29:859\u0026ndash;866. https://doi.org/10.1007/s00167-020-06038-w\u003c/li\u003e\n\u003cli\u003eParratte S, Price AJ, Jeys LM, et al (2019) Accuracy of a New Robotically Assisted Technique for Total Knee Arthroplasty: A Cadaveric Study. J Arthroplasty 34:2799\u0026ndash;2803. https://doi.org/10.1016/j.arth.2019.06.040\u003c/li\u003e\n\u003cli\u003eAlessi A, Fitzcharles E, Weber IC, Cafferky NL (2021) The Functionality of a Novel Robotic Surgical Assistant for Total Knee Arthroplasty: A Case Series. Case Reports in Orthopedics 2021:6659707. https://doi.org/10.1155/2021/6659707\u003c/li\u003e\n\u003cli\u003eMayne AI, Rajgor H, Munasinghe C, et al (2024) The ROSA robotic-arm system reliably restores joint line height, patella height and posterior condylar offset in total knee arthroplasty. The Knee 48:1\u0026ndash;7. https://doi.org/10.1016/j.knee.2024.02.007\u003c/li\u003e\n\u003cli\u003eWininger AE, Lambert BS, Sullivan TC, et al (2023) Robotic-Assisted Total Knee Arthroplasty Can Increase Frequency of Achieving Target Limb Alignment in Primary Total Knee Arthroplasty for Preoperative Valgus Deformity. Arthroplast Today 23:101196. https://doi.org/10.1016/j.artd.2023.101196\u003c/li\u003e\n\u003cli\u003eLee JH, Jung HJ, Lee JK, et al (2023) Large Osteophytes over 10 mm at Posterior Medial Femoral Condyle Can Lead to Asymmetric Extension Gap Following Bony Resection in Robotic Arm\u0026ndash;Assisted Total Knee Arthroplasty with Pre-Resection Gap Balancing. JCM 12:5980. https://doi.org/10.3390/jcm12185980\u003c/li\u003e\n\u003cli\u003eShin C, Crovetti C, Huo E, Lionberger D (2022) Unsatisfactory accuracy of recent robotic assisting system ROSA for total knee arthroplasty. J Exp Orthop 9:82. https://doi.org/10.1186/s40634-022-00522-7\u003c/li\u003e\n\u003cli\u003eEggermont E, Janssens R, Ulrix M, et al (2025) Sagittal accuracy and functional impact of tibial slope in imageless robotic-assisted Total Knee Arthroplasty. International Orthopaedics (SICOT). https://doi.org/10.1007/s00264-025-06472-w\u003c/li\u003e\n\u003cli\u003eLiddle AD, Pandit H, Judge A, Murray DW (2016) Effect of Surgical Caseload on Revision Rate Following Total and Unicompartmental Knee Replacement: The Journal of Bone and Joint Surgery 98:1\u0026ndash;8. https://doi.org/10.2106/JBJS.N.00487\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 369px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Patient demographics.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 255px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 369px;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003eValues\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 369px;\"\u003e\n \u003cp\u003eGender (male:female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003e3:14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 369px;\"\u003e\n \u003cp\u003eLeft:right\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003e7:10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 369px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 369px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 255px;\"\u003e\n \u003cp\u003e53.47 \u0026plusmn; 8.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 369px;\"\u003e\n \u003cp\u003eMean HKA angle (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 255px;\"\u003e\n \u003cp\u003e3.68 \u0026plusmn; 4.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 369px;\"\u003e\n \u003cp\u003eVarus:Valgus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003e14:2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 624px;\"\u003e\n \u003cp\u003e\u003cem\u003eA positive value denotes varus alignment; SD: standard deviation; HKA: hip\u0026ndash;knee\u0026ndash;ankle.\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Intra- and inter-class correlation coefficients (ICC) results for HKA and coronal laxity measurements during the acquisition step.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"558\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eInter-rater agreement (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eIntra-rater reliability (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHKA (\u0026deg;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0.979 (0.943 \u0026ndash; 0.992)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e0.985 (0.957 \u0026ndash; 0.995)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExtension (0\u0026deg;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eMaximum Extension (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0.938 (0.327 \u0026ndash; 0.985)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e0.987 (0.964 \u0026ndash; 0.995)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eMedial Laxity (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0.937 (0.836 \u0026ndash; 0.977)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e0.781 (0.499 \u0026ndash; 0.914)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eLateral Laxity (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0.887 (0.703 \u0026ndash; 0.958)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e0.857 (0.648 \u0026ndash; 0.946)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eVarus Stress (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0.973 (0.927 \u0026ndash; 0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e0.982 (0.951 \u0026ndash; 0.993)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eValgus Stress (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0.913 (0.662 \u0026ndash; 0.972)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e0.919 (0.791 \u0026ndash; 0.970)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eLaxity profile: Absolute (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0.624 (0.202 \u0026ndash; 0.847)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e0.729 (0.392 \u0026ndash; 0.893)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFlexion (90\u0026deg;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eMaximum Flexion (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0.937 (0.836 \u0026ndash; 0.977)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e0.781 (0.499 \u0026ndash; 0.914)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eMedial Laxity (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0.879 (0.696 \u0026ndash; 0.954)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e0.949 (0.866 \u0026ndash; 0.981)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eLateral Laxity (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0.786 (0.498 - 0.917)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e0.824 (0.588 \u0026ndash; 0.932)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eVarus Stress (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0.876 (0.689 \u0026ndash; 0.953)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e0.960 (0.894 \u0026ndash; 0.985)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eValgus Stress (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0.895 (0.708 \u0026ndash; 0.962)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e0.882 (0.642 \u0026ndash; 0.959)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eLaxity profile: Absolute (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0.653 (0.279 \u0026ndash; 0.858)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e0.855 (0.614 \u0026ndash; 0.947)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe ICC values indicate excellent agreement (\u0026gt;0.75) and good agreement (\u0026gt;0.60). Laxity profile: total range of laxity, expressed in absolute values, calculated based on the extreme values obtained during varus and valgus assessments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e Intra- and inter-class correlation coefficients (ICC) results for the positioning of implant components, final HKA, and implant size estimation during the planning step.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"586\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 204px;\"\u003e\n \u003cp\u003eInter-rater agreement (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eIntra-rater reliability (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 586px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemur\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eFlexion (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 204px;\"\u003e\n \u003cp\u003e0.211 (-0.317 \u0026ndash; 0.626)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e0.874 (0.692 \u0026ndash; 0.952)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eStylus height\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 204px;\"\u003e\n \u003cp\u003e0.482 (0.024 \u0026ndash; 0.775)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e0.707 (0.352 \u0026ndash; 0.884)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eDistal Varus/valgus (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 204px;\"\u003e\n \u003cp\u003e0.763 (0.453 \u0026ndash; 0.908)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e0.85 (0.633 \u0026ndash; 0.943)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eDistal medial resection (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 204px;\"\u003e\n \u003cp\u003e0.521 (0.074 \u0026ndash; 0.795)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e0.696 (0.335 \u0026ndash; 0.878)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eDistal lateral resection (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 204px;\"\u003e\n \u003cp\u003e0.803 (0.544 \u0026ndash; 0.923)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e0.924 (0.799 \u0026ndash; 0.972)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003ePosterior medial resection (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 204px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003ePosterior lateral resection (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 204px;\"\u003e\n \u003cp\u003e0.755 (0.44 \u0026ndash; 0.904)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e0.975 (0.934 \u0026ndash; 0.991)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eRotation from PCA (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 204px;\"\u003e\n \u003cp\u003e0.825 (0.577 \u0026ndash; 0.933)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e0.948 (0.864 \u0026ndash; 0.981)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 586px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTibia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eSlope (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 204px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eProximal varus/valgus (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 204px;\"\u003e\n \u003cp\u003e0.87 (0.682 \u0026ndash; 0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e0.896 (0.704 \u0026ndash; 0.963)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eProximal medial resection (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 204px;\"\u003e\n \u003cp\u003e0.786 (0.509 \u0026ndash; 0.916)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e0.924 (0.791 \u0026ndash; 0.973)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eProximal lateral resection (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 204px;\"\u003e\n \u003cp\u003e0.875 (0.577 \u0026ndash; 0.933)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e0.937 (0.741 \u0026ndash; 0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFinal planification\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 204px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eHKA (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 204px;\"\u003e\n \u003cp\u003e0.751 (0.436 \u0026ndash; 0.902)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e0.651 (0.279 \u0026ndash; 0.856)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eFEMUR - Implant size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 204px;\"\u003e\n \u003cp\u003e0.969 (0.918 \u0026ndash; 0.989)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003ePE - Implant size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 204px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe ICC values indicate excellent agreement (\u0026gt;0.75) and good agreement (\u0026gt;0.60).\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-orthopaedics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [International Orthopaedics](https://link.springer.com/journal/264)","snPcode":"264","submissionUrl":"https://submission.springernature.com/new-submission/264/3","title":"International Orthopaedics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6252951/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6252951/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose: \u003c/strong\u003eThis study quantifies the reproducibility of soft tissue laxity and gap measurements under manual stress, and implant positioning planning using the imageless ROSA® robotic system, by comparing a senior high-volume surgeon with a low-volume resident.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eIn this single-center prospective study, 17 patients undergoing robotic-assisted total knee arthroplasty were evaluated. Intra- and inter-operator variability was assessed by recording intraoperative measurements and planning outcomes using a standardized protocol for functional alignment (FA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eGood-to-excellent reproducibility in soft tissue and gap assessments is demonstrated by both intra- and inter-operator measurements. Minor differences in planning parameters—including stylus height, femoral implant flexion, and the distal femoral cut—are observed, likely due to subjective high-volume surgeon adjustments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eHigh reproducibility in soft tissue measurements and surgical planning across surgeons with different experience levels is shown by the ROSA® robotic system, while flexibility for individualized surgical strategies is retained.\u003c/p\u003e","manuscriptTitle":"Inter- and intra-operator variability in ligament balance measurements in total knee arthroplasty with the robotic navigation system (ROSA®): in vivo study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-21 08:34:32","doi":"10.21203/rs.3.rs-6252951/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-12T20:36:42+00:00","index":"","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-29T04:27:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-28T12:31:19+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-28T12:29:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Orthopaedics","date":"2025-03-18T11:55:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-orthopaedics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [International Orthopaedics](https://link.springer.com/journal/264)","snPcode":"264","submissionUrl":"https://submission.springernature.com/new-submission/264/3","title":"International Orthopaedics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"7eca72e0-ce09-475b-b7b0-87632b744b50","owner":[],"postedDate":"April 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-30T12:23:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-21 08:34:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6252951","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6252951","identity":"rs-6252951","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}