Augmented reality in knee arthroplasty: a scoping review of the current evidence

Introduction

Total knee arthroplasty (TKA) is the gold standard treatment for end-stage knee osteoarthritis (1,2). Despite optimal long-term survivorship rates—higher than 95% after 15 years according to systematic reviews and national data registries—about 20% of patients are unsatisfied after surgery (3-6). The current challenges in TKA include a 20% dissatisfaction rate among patients, partly due to difficulty in restoring native knee kinematics and achieving optimal component alignment. AR technology has the potential to address these challenges by providing real-time visualization and precise guidance during surgery (3-7). Patient dissatisfaction is multifactorial, with challenges arising from the difficulty in restoring the “complex” native kinematics and the importance of achieving correct component positioning (7).

In recent decades, advances in computer-assisted surgeries have been introduced into clinical practice. According to the most recent data published in the 2023 Australian Orthopaedic Association National Joint Replacement Registry report, 65.8% of TKAs are “technology-assisted”, including navigation, image-derived instrumentation, and robotic assistance (8). Computer-assisted surgery aims to provide intraoperative information, such as soft tissue tension, and improve the accuracy of component positioning, ultimately enhancing the overall outcome of the procedure (9,10).

One of the latest innovations in the medical field is the introduction of augmented reality (AR) in surgical procedures (11,12). Unlike virtual reality (VR), which creates a complete virtual experience, AR blends virtual elements with the real-world environment. AR devices are based on three main components: the virtual image (which could be created from a CT or MRI in image-based systems or re-created from intraoperative mapping in image-less systems), the registration of the model and its match with the virtual environment, and the display technology through which AR is viewed [display technology can be divided into two main categories: the head-mounted display (HMD) and enhanced external displays like monitors or smartphones] (13-15).

This technology seems promising for clinical application in orthopedic surgery due to the possibility of superimposing real-time radiographs, axes, preoperative planning, cutting blocks, and other relevant information into the surgical field while performing surgical actions (13-15). Preclinical and clinical studies have provided interesting results in acetabular component placement in total hip arthroplasty (THA) (16,17). A recent systematic review that included five studies compared the accuracy of acetabular component inclination and version between 196 AR THA and 200 conventional THA (17). The study concluded that accuracy was more significant in AR-assisted surgery for inclination and anteversion without increasing the overall surgical time and intraoperative blood loss. AR has also been applied to spine surgery for pedicle screw placement, cervical foraminotomy, and percutaneous surgery (18,19).

Despite these advancements, significant knowledge gaps remain in the application of AR for TKA. These include the lack of standardized protocols, limited long-term follow-up data, and the scarcity of PROMs. Unlike previous reviews, which primarily focus on isolated outcomes or specific applications, this review provides a comprehensive scoping of clinical and preclinical evidence. It highlights AR’s unique contributions to improving surgical precision while identifying limitations and areas for future research.

The aim of this study is to review the current literature on the use of AR devices in both clinical and preclinical settings of TKA. Specifically, we focused on three main points: (I) describing the different AR devices used for TKA; (II) reviewing the outcomes in terms of accuracy in preclinical settings; and (III) reviewing all studies that utilized AR in clinical practice for both TKA and unicompartmental knee arthroplasty (UKA). We present this article in accordance with the PRISMA-ScR reporting checklist (available at https://asj.amegroups.com/article/view/10.21037/asj-24-42/rc).

Methods

Search strategy

For this scoping review, relevant studies were searched in the Medline (PubMed), Scopus, Web of Science, and Google Scholar databases using the following key terms: “augmented reality”, “AR”, “knee arthroplasty”, and “knee replacement”. Articles published between January 2013 and October 2023 were included. The last search was performed on October 15, 2023. The search strategy followed PRISMA-ScR guidelines to ensure transparency and reproducibility. The literature search was conducted independently by two authors (G.C. and D.V). In cases of disagreement, a senior author (L.S.) was consulted for resolution. The strategy of article searched is shown in Table S1.

Selection criteria

This review includes preclinical and clinical research related to the application of AR technology in TKA. We included studies on surgical techniques, technical reports, case reports, and case series. Only studies published in English were included. We excluded studies where AR technology was not applied to TKA, as well as studies that used virtual or mixed reality in TKA.

Data extraction

All relevant information was extracted into a predesigned Excel worksheet. The data included the name of the first author and the year of publication, the type of study, the AR device utilized (including relevant characteristics such as image-based or imageless systems, and open or closed platforms), intervention procedures, radiographic outcomes analyzed, and clinical outcomes analyzed. Data extraction was performed by two authors (G.C. and D.V.). In case of disagreement, the senior author (L.S.) was consulted.

Statistical analysis

Descriptive statistical analysis was performed for all data extracted by the included studies. Mean values with a measure of variability as standard deviation (SD) or range (minimum-maximum) were calculated for continuous variables. Absolute number and frequency distribution were calculated for categorical variables. Furthermore, when available, the P values of the variable analyses comparisons of the different included in the various studies were reported.

Results

Literature search

Initially, a total of 232 potentially relevant articles were retrieved from the query. Titles and abstracts were screened, leaving 19 studies for final analysis. After assessed for eligibility, 11 studies were included in the analysis (20-30) (Figure 1). Key findings and summary of the papers included in the scoping review are reported in Table S2.

Figure 1 Flow diagram of included studies.

Five of the included studies reported the technical notes on the use of AR devices (20-24), five performed an accuracy analysis in a preclinical setting, mainly on sawbone models (20,23-26), and six reported accuracies in a clinical setting [five studies for TKA (25,26-28,30) and one for UKA (27)].

Iacono et al. (26) presented a case series of 5 patients and reviewed the evidence of AR in knee arthroplasty in 2021, while Fucentese et al. described the technical note of an AR device (24).

Wang et al. described a CT-based open platform system that uses the HoloLens (Microsoft Corporation) to superimpose a three-dimensional model based on preoperative planning (22). This system uses a technology based on an iterative closest point (ICP) model. A similar model was described by Pokhrel et al. (21) in 2018. This system was also CT-based and used the same ICP algorithm to superimpose the correct position of the cutting guide onto the surgical field based on preoperative three-dimensional planning displayed on a transparent screen in the surgical field.

A different model was described by Daniel et al. (20) in 2016. This system was not based on the registration of preoperative imaging but utilized imageless registration with markers attached to the tibia and femur, defining the six degrees of movement of the knee.

Tsukuda et al. (23,25,27,28) described the application of the AR Knee system to TKA; a similar system called AR Hip was already used and validated for acetabular component orientation in THA (31,32). The AR-assisted navigation system allows the generation of computer-generated images in the real world, showing results on a display. It consists of a smartphone and a resin marker with a bi-dimensional barcode. The AR Knee system can be used for both tibial and femoral cuts. The AR Knee system allows the surgeon to visualize the aiming varus/valgus and posterior slope cuts in the surgical field for the tibia. The marker is placed on the proximal tibia (proximal to the superior margin of the pes anserinus). The surgeon then registers bone landmarks using a pointer, while the smartphone interprets the positional relationship between markers and bony landmarks, recreating the tibial coordinate system. All relevant information is visible on the smartphone display. After tibial resection, the surgeon can check the accuracy of the cuts by placing an oval marker on the tibial surface. For the femur, the AR Knee system allows the surgeon to visualize the center of the femoral head, the femoral mechanical axis, the varus/valgus angle on the coronal plane, and the flexion/extension angle in the sagittal plane superimposed on the surgical field through the display of the smartphone. A resin marker is placed in a stable location in the surgical field (such as a Mayo table). The surgeon then inserts the spike of the dedicated jig into the mediolateral width of the distal part of the femur and attaches a smartphone to the jig. The spike has anterior and posterior fins that need to be aligned to the Whiteside line. While viewing the marker through the smartphone, the surgeon rotates internally, externally, and in flexion and extension to determine the center of rotation of the femoral head. After registration, both the femoral center of rotation and mechanical axis are displayed on the smartphone (23,25-28).

NextAR (by Medacta International, Castel San Pietro, Switzerland) is an AR surgical guidance system that intraoperatively measures lower limb alignment, prosthesis position, and soft tissue balance in TKA (24). NextAR is also available for shoulder arthroplasty and spine surgery (33,34). The system’s footprint is minimal, requiring only a pair of smart glasses, two small single-use sensors, and a control unit. During surgery, the information is displayed on a standard screen or via smart glasses, allowing real-time visualization of relevant information while performing surgical actions. This CT-based system enables the surgeon to analyze lower limb alignment and perform three-dimensional surgical planning before the procedure. The CT scan is uploaded to a closed system where company engineers reconstruct a 3D model of the knee and create preliminary planning based on the surgeon’s preference (mechanical or kinematical alignment). Camera holders and trackers are pinned to the bone, then the femur and tibia are registered (26 points per bone, with an approximate time of 3 to 5 minutes). After registration, the elongation of the medial collateral and lateral collateral ligaments is assessed. This additional information can be used intraoperatively to adjust the preoperative planning. The surgeon can choose the order of resection according to their preferences (distal cut, posterior cut, and tibial cut). The AR system assists the surgeon with the positioning of the cutting guides.

Three authors described the use of the Knee+ system (Pixee Medical, Besançon, France) (26,29,30). It allows the surgeon to view the tibial and femoral axes in the surgical field through smart glasses or via a laptop. This system comprises smart glasses worn during surgical procedures, two optical markers attached to the patient’s tibia and femur, and a smart touchscreen device. First, the surgeon inserts a tipped pin on the tibial guide along the anatomical axis of the tibia. Then, the bony landmarks of the medial and lateral malleoli are registered. After registration, the surgeon can position the cutting block by adjusting the varus/valgus and the slope based on their preference. After resection, a check of the executed cuts can be performed using a marker. For the femur, a pin is inserted at the conventional entry point. A guide QR code is placed on the surgical table, and the surgeon pivots the femur with a circumduction movement of the hip to identify the femoral center of rotation. After registration, the femoral mechanical axis is visible on the glasses or screen. The surgeon can then position the cutting block for the distal femur while acquiring real-time information about the varus/valgus and flexion/extension positions. The main characteristics of the studies included in the analysis are reported in Table 1.

Accuracy of AR systems in preclinical studies

Daniel et al. (20) measured the error of the AR system by evaluating the difference between the real distance between two objects and the distance measured with AR. They reported that the error increased exponentially as the distance between the markers in-creased. An “acceptable” error, less than 1%, was reported when the distance between the two markers was less than 30 cm (4 mm), but the best scenario (with an error of 0.8 mm) was reported when the distance was less than 20 mm. Optimal results were also reported by Pokhrel et al. (21), who noted a standard error of bone resection between 0.40 and 0.55 mm. Wang et al. (22) reported very interesting results in terms of precision, with a root mean square error of 1.08 mm and 1.78 mm for tibia and femur registration, respectively.

Two studies from the same group evaluated the accuracy of the AR-Knee system on sawbones (23,25). Tsukuda et al. (23) evaluated the accuracy of AR-Knee by resecting the proximal tibia in 10 pairs of tibial sawbones, aiming at a cut perpendicular to the long axis of the tibia (5° of posterior inclination and a 10-mm depth). They observed a difference between the values displayed intraoperatively and the actual measurements of 0.5°±0.2° for tibial varus/valgus, 0.8°±0.9° for slope, and 0.6±0.7 mm for thickness. Similar results for precision were reported for the distal femoral cut on ten femoral sawbones, with a difference between planned and measured values of 0.8°±0.5° for coronal plane orientation and 0.6°±0.5° for sagittal plane orientation (25).

Accuracy of AR in TKA and UKA

Tibial cut accuracy in TKA

Three studies reported the accuracy of tibial cuts with the same AR device (Knee+ System) (26,29,30). The average error between the planned and measured tibial cut in the coronal plane was 0.66°, while it was 0.89° in the sagittal plane. Castellarin et al. (30) reported the radiographic accuracy for proximal tibial cuts in 76 patients undergoing mobile bearing TKA (asymmetrical lateral sliding mobile insert, Genus MB by Adler Ortho, Cormano, Italy). They performed a tibia-first resection aiming to maintain the patient’s joint line (varus cut on the coronal plane in most cases). They recorded the difference between the preplanned cutting angles and the post-cut angles, finding a difference of 0.59°±0.55° in the coronal plane and 0.70°±0.75° for the tibial slope. Additionally, they noted that the error was less than 1° in 97% of patients in the coronal plane and 88% of patients in the sagittal plane. Iacono et al. (26) reported an average error in the tibial cut of less than 1° for the coronal plane angle (0.2°±0.8°) and less than 2° for the sagittal angle (1.4°±0.8°) in five cases. Bennett et al. (29) aimed to position the component in the coronal plane to compensate for the preoperative hip-knee angle and joint line obliquity, and to achieve a tibial slope of 7°. They reported a difference between the planned and measured angles of 1.1° (no outliers >3°) in the coronal plane and 1.6° (3 cases of outliers >3°) in the sagittal plane (Table 2).

Femoral cut accuracy in TKA

Four studies reported the accuracy of femoral cuts using AR, two of which used the Knee+ AR system (26,29), and two used the AR Knee system (25,28). The average error between the planned and measured femoral cuts in the coronal plane was 1.1°, while it was 0.86° in the sagittal plane. Tsukada et al. (28) performed a non-inferiority analysis comparing the alignment of femoral prostheses obtained with AR (109 knees) versus accelerometer-based navigation (119 knees). They aimed to position the femoral component perpendicular to the femoral mechanical axis. They reported a success rate, defined as alignment within 3° from the femoral mechanical axis, of 95.4% in the AR group and 93.2% in the accelerometer-based navigation group. They concluded that the AR system was non-inferior to navigation. In a previous study published in 2021, the same group (25) reported a difference between the angles visualized on the display and the measured angles of 0.8°±0.5° in the coronal plane and 0.6°±0.5° in the sagittal plane. Iacono et al. (26), in a series of 5 knees with the Knee+ AR device, reported an error of less than 1° for the coronal plane cut (0°±0°) and less than 2° for the sagittal plane (1.2°±0.8°). Bennett et al. (29), using the same device (Knee+ AR), aimed to restore the prearthritic joint-line obliquity of the femur, reporting an error of 1.3° (no outliers >3°) in the coronal plane and 2° (5 cases of outliers) in the sagittal plane in a series of 20 TKA (Table 2).

Tibial cut accuracy in UKA

Tsukuda et al. (25) performed a case series of 11 consecutive unicompartmental knee replacements using the AR Knee System for proximal tibial resection. They aimed to align the distal cut perpendicular to the long axis of the tibia in patients with a preoperative varus of less than 6°, and between 2° and 3° for patients with a preoperative varus greater than 6° in the coronal plane, and to maintain the native slope in the sagittal plane (or reduce the slope if the preoperative value was greater than 6°). They reported an average postoperative alignment in the coronal plane of 2.6°±1.2° of varus (absolute difference between preoperative target and postoperative measured angles of 1.9°±1.5°) and an average postoperative posterior slope of 4.8°±2.5° (absolute difference between preoperative target and postoperative measured angles of 2.6°±1.2°) (25).

Discussion

The most important finding of this review is the evidence supporting the accuracy and feasibility of AR technology in TKA. Through a comprehensive analysis of preclinical and clinical studies, the evaluation demonstrates that AR systems offer remarkable precision in bone resection and component placement. The average precision for both tibial and femoral cuts in the coronal and sagittal planes was within 1° (1.1° for femoral reductions of the coronal plane). There is no evidence regarding clinical outcomes or PROMs with this technology.

Several studies have focused on analyzing the accuracy of robotic systems in knee arthroplasty (35-37). Rossi et al. (35) described a case series of patients treated with the ROSA robotic system, demonstrating good accuracy in terms of the precision of the cuts and the planned versus resulting angles. The reported difference in the planned cuts on the femoral and tibial sides was less than 1 mm (SD <1). The difference between the overall hip-knee-ankle (HKA) alignment measured by ROSA and that measured on postoperative radiographs was 1.2°± 1.1° (35).

In a recent study, the accuracy of the Navio (image-less technique) and ROSA (image-based technique) robotic systems were compared (36). The sagittal orientation of the femoral components implanted with ROSA showed more frequent alterations, with greater extension compared to the components implanted with Navio. The reported difference in the coronal plane was 0.41°±0.48° in the Navio group and 0.47°±0.65° in the ROSA group. In the sagittal plane, it was 0.90°±0.80° (Navio) vs. 1.11°±0.75° (ROSA). On the tibial side, the reported differences were 0.43°±0.53° (Navio) vs. 0.59°±1.35° (ROSA) in the coronal orientation and 0.64°±0.51° (Navio) vs. 0.90°±0.59° (ROSA) in the sagittal plane. A possible explanation offered by the authors for a more significant difference in the sagittal plane is the use of the oscillating saw in ROSA, which is more prone to deviations compared to the burr used in Navio.

The precision and accuracy of the MAKO (by Stryker) system were analyzed by Sires et al. (37), demonstrating the system’s high precision: 94% of the cuts performed had an error of less than 1 mm compared to what was planned, with a very low standard deviation (<0.32). Deckey et al. (38) demonstrated the superiority in the orientation of prosthetic components using the same robotic system compared to patients treated by the same surgeon using a measured resection mechanical alignment TKA technique. In the analysis conducted, the coronal femoral orientation [0.9° (SD 1.2°) vs. 1.7° (SD 1.1°)], the coronal tibial orientation [0.3° (SD 0.9°) vs. 1.3° (SD 1.0°)], the sagittal tibial orientation [−0.3° (SD 1.3°) vs. 1.7° (SD 1.1°)], and the overall HKA with a set target of 0° [1.0° (SD 1.7°) vs. 2.7° (SD 1.9°)] were examined.

The AR systems’ accuracy is consistent when compared with reported studies involving different robotic and navigation systems. The non-inferiority of these AR systems in terms of accuracy is an encouraging result, showcasing their potential and providing a solid foundation for their future development.

The ability to visualize critical information directly in the operative field makes AR a valuable tool for surgical assistance. Intraoperative information, planning, ligament balancing, and the potential placement of cutting guides and components are all visualized in the same field of view, potentially reducing surgical times and increasing accuracy. Furthermore, AR systems, such as NextAR, require less space in the operating room compared to traditional robotic and navigation systems, improving efficiency and the intraoperative environment (24,26).

AR effectively assists the surgeon during the procedure by providing real-time feedback on the positioning of prosthetic components and precisely guiding the fixation of cutting guides or the components themselves. It also allows for the verification of the strain on collateral ligaments throughout the entire range of motion and how they change based on different orientations of the prosthetic components during planning and trials with test components. This enables effective knee balancing and verification of the graphs obtained from the AR device, allowing the surgeon to directly observe the knee’s range of motion and the actual balance achieved without having to take their eyes off the operative field (21-25).

This review highlights several critical limitations that must be addressed. Firstly, the substantial heterogeneity among the included studies—concerning study design, methodologies, and patient populations—complicates efforts to perform standardized analyses and weakens the ability to draw definitive conclusions. Furthermore, the limited generalizability of findings raises concerns regarding the applicability of AR technology across diverse clinical and surgical contexts. Another significant limitation lies in the varying quality of evidence, as several studies exhibit methodological flaws, such as small sample sizes, non-randomized designs, and a lack of long-term follow-up data. These issues not only affect the robustness of the conclusions but also limit the broader relevance of the findings. Notably, the absence of PROMs in most studies represents a major gap, as PROMs are essential for understanding AR’s real-world impact on patient satisfaction, functional recovery, and quality of life.

To overcome these limitations, future research must prioritize robust study designs and methodological rigor. High-quality, randomized controlled trials with standardized reporting protocols are essential to strengthen the evidence base. A particular focus should be placed on incorporating PROMs to assess patient satisfaction, functional recovery, and overall quality of life, while long-term follow-up studies will be necessary to evaluate the durability and clinical impact of AR technology over time. Additionally, cost-effectiveness analyses and scalability studies should be conducted to determine the feasibility of integrating AR systems into diverse surgical settings. Addressing these gaps will provide a more comprehensive understanding of AR’s clinical value and ensure its practical and widespread adoption in TKA. Rigorous research efforts are essential to optimize AR technology and enhance its role in improving surgical precision, patient outcomes, and healthcare efficiency.

Conclusions

The utilization of AR in TKA presents a promising frontier in orthopedic surgery, aiming to enhance surgical precision and outcomes. Through a comprehensive review of the literature, considering both preclinical and clinical studies, this investigation sheds light on the evolving landscape of AR technology in TKA. Various AR systems have been described, ranging from computed tomography (CT)-based open platform systems like the HoloLens to smartphone-based systems. These systems offer real-time visualization of anatomical structures and surgical plans, facilitating accurate component positioning and soft tissue balancing. Preclinical studies have demonstrated impressive precision in bone resection and component placement using AR, with reported errors within acceptable margins. Clinical studies further corroborate the potential of AR in improving surgical accuracy without significant increases in operative time or blood loss. These findings underscore the importance of implementing this new technology in TKA, heralding a future where such technologies become integral tools in optimizing patient outcomes and satisfaction. As AR continues to evolve and integrate into surgical practice, further research and advancements hold the promise of refining techniques, expanding indications, and improving the reporting of clinical outcomes.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the PRISMA-ScR reporting checklist. Available at https://asj.amegroups.com/article/view/10.21037/asj-24-42/rc

Peer Review File: Available at https://asj.amegroups.com/article/view/10.21037/asj-24-42/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://asj.amegroups.com/article/view/10.21037/asj-24-42/coif). G.C. serves as an unpaid editorial member of AME Surgical Journal from November 2024 to December 2026. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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