Mixed reality improves agreement on surgical approach selection and patient positioning in tibial plateau fracture planning compared to CT, 3DCT and 3D printing

Mixed reality improves agreement on surgical approach selection and patient positioning in tibial plateau fracture planning compared to CT, 3DCT and 3D printing | 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 Mixed reality improves agreement on surgical approach selection and patient positioning in tibial plateau fracture planning compared to CT, 3DCT and 3D printing Tobias Dust, Julian-Elias Henneberg, Maximilian Hartel, Alonja Reiter, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9259392/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Purpose Tibial plateau fractures require precise preoperative planning, particularly regarding treatment concept, patient positioning, and surgical approach. Mixed reality (MR) may enhance three-dimensional understanding of fracture morphology beyond conventional imaging and physical 3D models. This study investigated whether MR improves agreement on key preoperative planning decisions compared to computed tomography (CT), 3D computed tomography (3DCT), and 3D-printed fracture models. Methods Twelve orthopaedic trauma surgeons (6 junior, 6 senior) evaluated 22 surgically treated tibial plateau fractures (AO/OTA type B or C). Each case was assessed in four steps using (1) CT, (2) 3DCT reconstructions, (3) physical 3D-printed models, and (4) MR visualization using Microsoft Hololens 2. Raters selected (i) treatment concept, (ii) patient positioning, and (iii) surgical approach. Interobserver agreement was analysed using Fleiss’ kappa (κ) and percentage match (PM). Results Mixed Reality (MR) yielded the highest interobserver agreement for both surgical approach (PM 32%, κ = 0.30) and patient positioning (PM 57%, κ = 0.35), particularly among junior surgeons (approach PM 39%, κ = 0.28; positioning PM 57%, κ = 0.39). Compared to CT, 3DCT, and 3D printing, MR demonstrated consistent improvements, while agreement on treatment concepts remained high across all modalities (PM > 82%). Conclusion Mixed Reality (MR) improved interobserver agreement in preoperative planning for tibial plateau fractures, particularly for surgical approach selection and patient positioning. These effects were most pronounced among less experienced raters, highlighting MR’s role as a cognitive support tool and a promising instrument in surgical education. Compared to conventional imaging and 3D printing, MR provided dynamic interaction and greater planning flexibility. While further clinical validation is required, these findings support MR as a valuable adjunct in orthopaedic trauma planning. Level of evidence: Level II tibial plateau fracture mixed reality preoperative planning patient positioning surgical approach interobserver agreement Introduction The surgical treatment of tibial plateau fractures remains one of the most demanding tasks in orthopaedic trauma surgery 1 . Due to the complex three-dimensional fracture morphology and frequent involvement of posterior and posterolateral segments, accurate preoperative planning is essential to achieve anatomical reduction, stable fixation, and appropriate functional outcomes 2 – 5 . In particular, the choice of treatment concept, patient positioning, and surgical approach plays a decisive role in enabling adequate exposure of the fracture site and avoiding intraoperative compromises that may negatively affect reduction quality and implant placement 6 – 9 . Computed tomography (CT) and three-dimensional computed tomography (3DCT) reconstructions have become standard tools for the assessment of tibial plateau fractures and substantially improved fracture visualization compared with conventional radiography 10 . Nevertheless, important aspects of spatial fracture understanding and fragment interrelation may remain difficult to appreciate using two-dimensional screens, especially in complex fracture patterns. To overcome these limitations, point-of-care three-dimensional (3D) printing has been introduced as an adjunctive technology for preoperative planning 11 – 14 . Previous studies have demonstrated that physical 3D-printed fracture models can enhance the surgeon’s spatial understanding and may improve agreement regarding patient positioning and surgical approach selection, particularly among less experienced surgeons 11 , 12 , 15 . However, 3D printing is associated with additional costs, production time, and logistical constraints, which may limit its routine clinical use 13 , 16 . More recently, immersive visualization technologies such as virtual reality (VR) and mixed reality (MR) have gained increasing attention in orthopaedic trauma surgery 17 – 20 . These technologies allow interactive three-dimensional visualization of patient-specific fracture anatomy without the need for physical model production. While VR and MR have already been investigated for fracture diagnostics and classification tasks 18 , 19 , evidence regarding their role in preoperative decision making remains limited. In particular, it is currently unclear whether MR can provide a comparable or even superior benefit to 3D printing when planning critical operative parameters such as treatment concept, patient positioning, and surgical approach. Therefore, the purpose of this study was to assess the impact of mixed reality on preoperative planning of tibial plateau fractures in comparison with CT, 3DCT, and physical 3D-printed models. Specifically, we aimed to analyze interobserver agreement regarding (1) treatment concept, (2) patient positioning, and (3) surgical approach selection using these different visualization modalities. We hypothesized that mixed reality would improve agreement on key preoperative planning decisions compared with conventional imaging techniques and physical 3D-printed models, with potential differences across varying levels of surgical experience. Methods Study design This study was designed as a retrospective observer study evaluating the impact of different visualisation modalities on preoperative planning of tibial plateau fractures. Conventional computed tomography (CT), three-dimensional computed tomography (3DCT), physical 3D-printed fracture models, and mixed reality (MR) visualisation were compared with regard to agreement on key preoperative planning decisions. Fracture cases A total of 22 intra-articular tibial plateau fractures (AO/OTA type B or C) treated between 2017 and 2022 were included. Eligibility required availability of a preoperative CT scan with an axial slice thickness of 1 mm or less and complete multiplanar reconstructions. Cases with previous fractures of the proximal tibia or incomplete imaging data were excluded. All datasets were fully anonymised prior to analysis. Imaging data and three-dimensional reconstructions CT datasets were obtained from the institutional picture archiving and communication system (PACS) and stored in Digital Imaging and Communications in Medicine (DICOM) format. Raters reviewed axial, sagittal and coronal CT image stacks in a scrollable format simulating standard PACS navigation. Three-dimensional CT reconstructions of the proximal tibia and fibula were generated from the same datasets. The femur and patella were digitally removed to improve visualisation of the tibial plateau and fracture morphology. The 3D reconstructions were presented as rotatable models allowing free inspection from different perspectives. Segmentation and 3D printing Fracture segmentation was performed using Materialise Mimics Innovation Suite (version 24; Materialise, Leuven, Belgium) (Supplement 1). A threshold-based semi-automatic segmentation approach was applied to isolate osseous structures, followed by manual refinement in cases of comminution or depressed fragments. Post-processing was carried out using Materialise 3-matic Medical (version 16; Materialise, Leuven, Belgium) (Supplement 2, A & B), including surface smoothing and stabilisation of fracture fragments where required. The segmented fracture models were exported as standard tessellation language (STL) files and printed at a 1:1 scale using an Ultimaker S5 dual-head fused deposition modelling (FDM) printer (Ultimaker, Utrecht, The Netherlands) (Supplement 2, C & D). Polylactic acid (PLA) was used as the primary printing material, while water-soluble polyvinyl alcohol (PVA) served as support material. A layer thickness of 0.1 mm was selected to ensure high geometric accuracy and sufficient detail of the fracture morphology. After printing, support structures were removed prior to evaluation. The physical 3D-printed models were used exclusively for visual and tactile inspection during the respective study phase. No digital images or photographs of the printed models were provided to the raters. Mixed reality visualisation Mixed reality visualisation was performed using the Microsoft HoloLens 2 head-mounted display. Patient-specific 3D fracture models generated from the segmentation workflow were imported into the Materialise Viewer application (Supplement 3). Raters were able to interact with the virtual fracture models using standard MR gestures, including rotation, scaling, and free spatial positioning within the real-world environment. All raters received a standardised introduction to the MR system prior to the study. Raters Twelve orthopaedic trauma surgeons participated in the study, including six junior and six senior surgeons. All raters were blinded to the actual surgical treatment performed in the clinical cases. Study procedure Each rater evaluated all 22 fracture cases sequentially using four different visualisation modalities: CT, 3DCT, physical 3D-printed model, and mixed reality visualisation. The order of the modalities was identical for all raters. At each stage, raters were asked to independently determine three preoperative planning parameters: treatment concept, patient positioning, and surgical approach. Predefined answer options were provided for each parameter to ensure standardised assessment across raters using an online questionnaire (Supplement 4). Multiple surgical approaches could be selected if a combined approach was considered necessary. Raters were not permitted to revise previous answers after progressing to the next stage. Outcome measures The primary outcome measure was interobserver agreement for surgical approach selection. Secondary outcome measures included interobserver agreement for patient positioning and treatment concept selection. Statistical analysis Interobserver agreement was analysed using Fleiss’ kappa (κ) for each outcome parameter and visualisation modality. Kappa values were interpreted according to the criteria proposed by Landis and Koch (Table 1 ). In addition, percentage match (PM) was calculated as a descriptive measure of agreement, representing the proportion of identical ratings among raters. Table 1 Landis and Koch grading of reliability based on κ co-efficient values 33 κ co-efficient reliability grading 0.80 excellent Agreement analyses were performed for the entire rater cohort as well as separately for junior and senior surgeons. Statistical analyses were conducted using SPSS Statistics (version 26; IBM Corp., Armonk, NY, USA). Ethics approval The study was approved by the Ethics Committee of the Medical Chamber of Hamburg, Germany (ID: 2024-101279-BO-ff) and was conducted in accordance with the Declaration of Helsinki. All imaging data were anonymised prior to analysis. Results Interobserver agreement for surgical approach selection Interobserver agreement for surgical approach selection, defined as the primary outcome parameter, is summarised in Table 2 . Overall agreement was low to moderate across all visualisation modalities. Percentage match (PM) was 29% for both CT and 3DCT, increased to 33% for 3D-printed models, and reached 32% for mixed reality. Fleiss’ kappa values showed a comparable pattern, ranging from 0.22 for 3DCT to 0.30 for mixed reality. Table 2 interobserver agreements for the surgical approach Surgical approach CT 3DCT 3D MR PM κ PM κ PM κ PM κ Overall 29% 0.23 29% 0.22 33% 0.27 32% 0.30 Junior Surgeons 27% 0.19 27% 0.19 34% 0.26 39% 0.28 Senior Surgeons 33% 0.27 33% 0.26 32% 0.26 31% 0.21 When stratified by level of experience, junior surgeons demonstrated lower agreement across all modalities. In this subgroup, PM increased from 27% with CT and 3DCT to 34% with 3D-printed models and to 39% with mixed reality, while kappa values increased from 0.19 to 0.28. Among senior surgeons, agreement remained relatively stable across modalities, with PM values between 31% and 33% and kappa values between 0.21 and 0.27. Interobserver agreement for patient positioning Interobserver agreement for patient positioning is presented in Table 3 . Across all raters, PM was 46% for both CT and 3DCT, increased to 53% with 3D-printed models, and further increased to 57% with mixed reality. Corresponding kappa values were 0.25 for CT, 0.26 for 3DCT, 0.36 for 3D printing, and 0.35 for mixed reality. Table 3 interobserver agreements for the patient positioning Patient positioning CT 3DCT 3D MR PM κ PM κ PM κ PM κ Overall 46% 0.25 46% 0.26 53% 0.36 57% 0.35 Junior Surgeons 42% 0.19 43% 0.23 52% 0.35 57% 0.39 Senior Surgeons 55% 0.36 53% 0.33 55% 0.36 55% 0.27 Junior surgeons demonstrated a marked increase in agreement with advanced visualisation modalities. In this subgroup, PM increased from 42% with CT to 52% with 3D printing and to 57% with mixed reality, while kappa values increased from 0.19 to 0.39. In contrast, senior surgeons showed higher baseline agreement, with PM values ranging from 53% to 55% and kappa values between 0.27 and 0.36, without substantial differences between modalities. Interobserver agreement for treatment concept Interobserver agreement for treatment concept selection is shown in Table 4 . Agreement was high across all visualisation modalities. Overall percentage match (PM) values ranged from 82% to 91%, with the highest PM observed for mixed reality. Fleiss’ kappa values were low to moderate and ranged from 0.17 to 0.25 across modalities. Table 4 interobserver agreements for the treatment concept Treatment concept CT 3DCT 3D MR PM κ PM κ PM κ PM κ Overall 83% 0.23 83% 0.25 82% 0.17 91% 0.11 Junior Surgeons 82% 0.20 82% 0.21 81% 0.16 91% -0.3 Senior Surgeons 83% 0.24 84% 0.25 83% 0.15 92% 0.23 Similar patterns were observed in both junior and senior surgeons. Junior surgeons demonstrated PM values between 81% and 91%, whereas senior surgeons showed PM values between 83% and 92%. Kappa values remained within a comparable range across all modalities and experience levels. Discussion The primary finding of this study is that Mixed Reality (MR) improved interobserver agreement in preoperative planning for tibial plateau fractures (TPF), particularly in the domains of surgical approach selection and patient positioning. While agreement on treatment concepts was already high across all modalities MR showed the clearest improvements in surgical approach selection and patient positioning—two areas where interobserver agreement was lowest with conventional modalities. These benefits were most pronounced among less experienced raters, suggesting that MR offers a valuable cognitive support tool for surgical planning. Although we observed consistent trends favoring MR, the study was not powered to assess statistical significance. Correct patient positioning and surgical access are foundational to optimal outcomes in intra-articular fracture management. Mispositioning can compromise intraoperative visualization, increase operative time, and complicate access to key fracture zones 13 , 15 , 21 – 24 . In tibial plateau fractures—especially those involving posterior fragments—optimal positioning (e.g., prone vs. supine, flexion angle) determines whether an anatomic reduction can be achieved without excessive soft tissue dissection 25 . Likewise, choosing the correct surgical approach is critical for preserving soft tissue integrity, minimizing neurovascular injury, and enabling stable implant positioning 9 , 21 , 23 , 26 . Manidakis et al. further linked suboptimal treatment strategies to poor functional recovery and persistent postoperative pain 27 . Our findings support that MR, by enhancing spatial understanding, may reduce these risks through more accurate preoperative access planning. Although not measured directly in our study, this planning accuracy has the potential to translate into improved intraoperative efficiency and patient safety. Our findings align with and expand upon earlier work on 3D visualization in orthopaedic trauma care. Prior studies have shown that 3D printing can improve diagnostic confidence, spatial understanding, and decision-making in managing intra-articular fractures 12 , 13 , 15 , 19 . For instance, 3D printed models improved rater agreement in surgical approach and patient positioning decisions, especially among less experienced users 12 , 15 . Additional research on acetabular and pilon fractures supports similar trends 28 – 30 . MR appears to extend these benefits. In contrast to the static inspection of physical models, MR offers interactive and dynamic fracture exploration, allowing users to manipulate holograms within the surgical field of view. Prior evidence for the preoperative utility of MR in orthopaedics remains limited. Bitschi et al. evaluated MR in tibial plateau fractures and found that visualization with MR changed the fracture segment classification in 79% of cases using the 10-segment system, suggesting that dynamic holographic exploration may impact decision-making more than conventional modalities 18 . In contrast, Brouwers et al. conducted a study on acetabular fractures comparing virtual reality (VR) models to 3D printing 17 . Their findings revealed that VR did not surpass 3D printing in terms of planning accuracy or user confidence, highlighting limitations in the standardization and usability of immersive technologies. These contrasting results underscore the need to interpret MR outcomes in the context of hardware and software variation. While Brouwers et al. did not find a clear advantage of immersive environments over 3D printing in terms of planning accuracy, our results suggest that MR may offer superior value in dynamic planning contexts. Additionally, earlier work has demonstrated improved diagnostic accuracy and user confidence with MR in fracture classification, which further supports our findings in the preoperative planning domain 19 . Consistent with previous work, our results highlight that MR’s benefits are most pronounced among junior surgeons. This aligns with prior studys who found that 3D technologies enhanced spatial comprehension and improved decision-making in less experienced surgeons 11 , 12 , 15 , 19 , 28 , 31 . MR's immersive environment seems to provide an additional layer of cognitive support for early-career users. Interestingly, this benefit may be linked to a reduction in mental workload, as previously demonstrated by Lu et al., who used NASA-TLX metrics to assess MR-supported planning. Surgeons reported improved communication, spatial clarity, and lower frustration during decision-making when using MR environments 20 . These cognitive advantages may explain the particularly strong performance improvements in positioning and access planning observed in our junior subgroup. Senior surgeons benefit less markedly, likely due to their pre-existing spatial visualization and procedural planning skills. However, the high level of agreement and subjective utility reported by experienced raters suggests that MR may still serve as a valuable validation tool, even for experts. To date, few comparative studies have evaluated 3D printing and MR specifically in the context of preoperative planning for tibial plateau fractures 18 , 19 , 32 . While both technologies rely on similar upstream processes—namely high-resolution CT imaging and standardized segmentation—their downstream implementation differs considerably. Once segmentation is complete, MR models can be rendered and reviewed almost immediately, whereas 3D printing typically requires 6–8 hours of additional production time, depending on printer type and materials. This makes MR not only faster to implement but also less resource-intensive in acute or semi-urgent scenarios. In addition, MR environments such as those deployed with HoloLens 2 can be operated in sterile surgical settings due to their gesture-based interface and wearable design. In contrast, 3D printed models raise regulatory concerns regarding sterilization and OR compatibility, particularly when intended for intraoperative use. These practical differences highlight MR’s potential as a more flexible and scalable tool in both planning and intraoperative scenarios. Nevertheless, 3D printing continues to offer advantages in haptic feedback and interprofessional communication, particularly during surgical team discussions or patient consultations. Limitations Our study has several limitations. First, the CT scans were acquired at multiple institutions, and although all included datasets met the minimum resolution threshold for segmentation, variations in scan quality and protocol may have introduced heterogeneity in 3D model fidelity. However, to ensure consistency in model geometry, all DICOM datasets were processed using a standardised segmentation protocol and STL export workflow. Second, participant fatigue may have influenced scoring accuracy, as the rating process involved detailed review of 22 cases across four modalities. Although breaks were permitted and the sequence of cases randomized, cognitive fatigue cannot be fully excluded. Third, no formal training module was implemented prior to MR assessment. While participants were given brief instruction, variability in MR familiarity may have influenced performance—particularly among less experienced raters. To reduce this bias, the fractures were presented to all raters in a random order. Fourth, this was an interobserver reliability study and did not assess intrarater reproducibility or direct clinical outcomes such as intraoperative times, quality of reduction, or complication rates. However, the inclusion of a large and heterogeneous group of raters across all experience levels, combined with a standardized and blinded assessment protocol, strengthens the generalizability and robustness of the observed agreement patterns. Finally, variations in the technical capabilities of extended reality platforms (e.g., field of view, model resolution) complicate direct comparisons across studies. Standardization in MR model preparation and rendering remains an important prerequisite for multicenter validation. Despite these limitations, the use of high-quality segmentation protocols, multiple raters of varying experience, and comparison across four modalities provides a robust foundation for interpreting the observed improvements in agreement and planning confidence. Outlook Future research should focus on the technical and clinical standardization of MR-assisted planning in orthopaedic trauma care. To enable comparability across studies, consensus is needed on hardware specifications, file formats, rendering quality, and segmentation protocols for extended reality applications. Comparative studies that benchmark various MR platforms and software environments will be essential to define technical benchmarks and usability standards. Furthermore, it is essential to define and investigate meaningful clinical outcome parameters that are currently lacking in most MR studies. Multicenter, prospective trials should integrate MR with intraoperative navigation systems to evaluate workflow compatibility and to explore potential synergy between technologies. Conclusion Mixed Reality (MR) enhances interobserver agreement in key domains of preoperative planning for tibial plateau fractures, particularly in surgical approach selection and patient positioning. These benefits were most notable among less experienced surgeons, underscoring the value of MR as a cognitive support tool. Compared to conventional imaging and 3D printing, MR offers dynamic interaction, greater flexibility, and workflow efficiency. While further clinical studies are required to validate its intraoperative and patient-centered benefits, our findings support the integration of MR into modern orthopaedic trauma planning as a valuable adjunct across experience levels. Declarations Acknowledgements Author contributions All authors drafted the manuscript. T.D., J.H., A.K., and J.K. were responsible for patient selection and 3D printing preparation. T.D., J.H., M.K., and M.H. designed the questionnaire and online survey tool. T.D., J.H., and A.R. performed data collection and statistics. T.D., J.H., and A.S. designed the figures. M.K., K.F., and J.K. revised the manuscript. K.F., M.K., and A.S. provided the theoretical background. All authors read and approved the final version of the manuscript. Funding No funding was received for conducting this study. Competing Interests The authors declare no competing interests. Data availability statement The datasets generated and/or analysed during the current study are not publicly available due to institutional data protection policies but are available from the corresponding author on reasonable request. Ethikal approval This study was approved by the Ethics Committee of the Medical Chamber of Hamburg, Germany (ID: 2024-101279-BO-ff) and conducted in accordance with the Declaration of Helsinki. All participants provided written informed consent prior to their participation in the study. References Raschke MJ, Herbst E. Tibial plateau fractures: a lot more to come! Eur J Trauma Emerg Surg. 2020;46:1201–2. 10.1007/s00068-020-01551-6 . Meulenkamp B, et al. Risk Factors, and Location of Articular Malreductions of the Tibial Plateau. J Orthop Trauma. 2017;31:146–50. 10.1097/BOT.0000000000000735 . Incidence. van Dreumel RL, van Wunnik BP, Janssen L, Simons PC, Janzing HM. Mid- to long-term functional outcome after open reduction and internal fixation of tibial plateau fractures. Injury. 2015;46:1608–12. 10.1016/j.injury.2015.05.035 . Honkonen SE. Degenerative arthritis after tibial plateau fractures. J Orthop Trauma. 1995;9:273–7. 10.1097/00005131-199509040-00001 . Krause M, et al. Intra-articular tibial plateau fracture characteristics according to the Ten segment classification. Injury. 2016;47:2551–7. 10.1016/j.injury.2016.09.014 . Egol KA, Tejwani NC, Capla EL, Wolinsky PL, Koval KJ. Staged management of high-energy proximal tibia fractures (OTA types 41): the results of a prospective, standardized protocol. J Orthop Trauma. 2005;19:448–55. 10.1097/01.bot.0000171881.11205.80 . discussion 456. Cho JW, et al. Approaches and fixation of the posterolateral fracture fragment in tibial plateau fractures: a review with an emphasis on rim plating via modified anterolateral approach. Int Orthop. 2017;41:1887–97. 10.1007/s00264-017-3563-6 . Orapiriyakul W, Apivatthakakul T, Phornphutkul C. Posterolateral tibial plateau fractures, how to buttress? Reversed L posteromedial or the posterolateral approach: a comparative cadaveric study. Arch Orthop Trauma Surg. 2018;138:505–13. 10.1007/s00402-018-2875-3 . Alessio-Mazzola M, Placella G, Conca M, Salini V. Posterolateral tibia plateau fractures: pros and cons of different surgical approaches. EFORT Open Rev. 2025;10:416–23. 10.1530/eor-2025-0037 . Castiglia MT, et al. The Impact of Computed Tomography on Decision Making in Tibial Plateau Fractures. J Knee Surg. 2018;31:1007–14. 10.1055/s-0038-1627464 . Dust T, et al. The influence of 3D printing on inter- and intrarater reliability on the classification of tibial plateau fractures. Eur J Trauma Emerg Surg. 2022. 10.1007/s00068-022-02055-1 . Dust T, et al. 3D printing improves preoperative decision making for patient positioning and surgical approach selection for tibial plateau fractures. Sci Rep. 2025;15:26513. 10.1038/s41598-025-11995-8 . Assink N, et al. Does 3D-assisted surgery of tibial plateau fractures improve surgical and patient outcome? A systematic review of 1074 patients. Eur J Trauma Emerg Surg. 2022;48:1737–49. 10.1007/s00068-021-01773-2 . Giannetti S, Bizzotto N, Stancati A, Santucci A. Minimally invasive fixation in tibial plateau fractures using an pre-operative and intra-operative real size 3D printing. Injury. 2017;48:784–8. 10.1016/j.injury.2016.11.015 . Huitema JM, et al. Are 3D-printed Models of Tibial Plateau Fractures a Useful Addition to Understanding Fractures for Junior Surgeons? Clin Orthop Relat Res. 2022;480:1170–7. 10.1097/CORR.0000000000002137 . Ballard DH, et al. Medical 3D Printing Cost-Savings in Orthopedic and Maxillofacial Surgery: Cost Analysis of Operating Room Time Saved with 3D Printed Anatomic Models and Surgical Guides. Acad Radiol. 2020;27:1103–13. 10.1016/j.acra.2019.08.011 . Brouwers L, et al. What is the value of 3D virtual reality in understanding acetabular fractures? Eur J Orthop Surg Traumatol. 2020;30:109–16. 10.1007/s00590-019-02537-w . Bitschi D, et al. Preoperative Mixed-Reality Visualization of Complex Tibial Plateau Fractures and Its Benefit Compared to CT and 3D Printing. J Clin Med. 2023;12. 10.3390/jcm12051785 . Dust T, et al. Mixed reality enhances interobserver reliability in tibial plateau fracture classification compared to computed tomography, 3D computed tomography and 3D printing. J Experimental Orthop. 2025;12:e70474. https://doi.org/10.1002/jeo2.70474 . Lu L, et al. Applications of Mixed Reality Technology in Orthopedics Surgery: A Pilot Study. Front Bioeng Biotechnol. 2022;10:740507. 10.3389/fbioe.2022.740507 . Krause M, Krüger S, Müller G, Püschel K, Frosch KH. How can the articular surface of the tibial plateau be best exposed? A comparison of specific surgical approaches. Arch Orthop Trauma Surg. 2019. 10.1007/s00402-019-03200-z . Ganguli A, et al. 3D printing for preoperative planning and surgical training: a review. Biomed Microdevices. 2018;20:65. 10.1007/s10544-018-0301-9 . Bear J, Diamond O, Helfet D. Strategies for Success in Plating of Complex Proximal Tibia Fractures. Oper Tech Orthop. 2018;28:157–63. https://doi.org/10.1053/j.oto.2018.07.006 . Nie W, et al. Preliminary application of three-dimension printing technology in surgical management of bicondylar tibial plateau fractures. Injury. 2019;50:476–83. 10.1016/j.injury.2018.12.019 . Krause M, Frings J, Isik H, Frosch KH. Comparison of extended lateral approaches to the tibial plateau: The articular exposure of lateral epicondyle osteotomy with and without popliteus tendon vs. fibula osteotomy. Injury. 2020;51:1874–8. 10.1016/j.injury.2020.05.038 . Boluda-Mengod J, et al. A proposal of a new algorithm for decision-making approaches in open reduction and internal fixation of complex tibial plateau fractures - SOTA algorithm (Spanish Orthopaedic Trauma Association). Injury. 2021;52(Suppl 4):S87–98. 10.1016/j.injury.2021.03.064 . Manidakis N, et al. Tibial plateau fractures: functional outcome and incidence of osteoarthritis in 125 cases. Int Orthop. 2010;34:565–70. 10.1007/s00264-009-0790-5 . Brouwers L, et al. The Value of 3D Printed Models in Understanding Acetabular Fractures. 3D Print Additive Manuf. 2018;5:37–46. 10.1089/3dp.2017.0043 . Phelps KD, et al. A Randomized Trial of Preoperative Practice in a Simulated Operative Environment: 3D Printed Pilon Model Improves Resident Performance. J Surg Orthop Adv. 2022;31:187–92. Bai J, et al. Efficacy and safety of 3D print-assisted surgery for the treatment of pilon fractures: a meta-analysis of randomized controlled trials. J Orthop Surg Res. 2018;13:283. 10.1186/s13018-018-0976-x . Wu AM, et al. The addition of 3D printed models to enhance the teaching and learning of bone spatial anatomy and fractures for undergraduate students: a randomized controlled study. Ann Transl Med. 2018;6:403. 10.21037/atm.2018.09.59 . Colcuc C, et al. Comparison of virtual reality and computed tomography in the preoperative planning of complex tibial plateau fractures. Arch Orthop Trauma Surg. 2024;144:2631–9. 10.1007/s00402-024-05348-9 . Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977;33:159–74. Additional Declarations No competing interests reported. Supplementary Files Supplement.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 25 Apr, 2026 Reviews received at journal 25 Apr, 2026 Reviews received at journal 21 Apr, 2026 Reviewers agreed at journal 15 Apr, 2026 Reviewers agreed at journal 14 Apr, 2026 Reviewers invited by journal 13 Apr, 2026 Editor assigned by journal 12 Apr, 2026 Submission checks completed at journal 02 Apr, 2026 First submitted to journal 29 Mar, 2026 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-9259392","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":623843369,"identity":"d9021ebb-3590-4d44-b203-bb33580911ce","order_by":0,"name":"Tobias Dust","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAApklEQVRIiWNgGAWjYHACAyC2YZAAkhJEqecBa0lII13LYRK02DMwb5P8+eN84sz2BsYbH4izha1MmifhduJsngPMljOI08JjJs0A1DJPIoFNmodYLZI/Es4lzpN/wCb9h1gtEjwJBxJnSzCwSROjg4HnMFuxNU9asvHMnsRmyx5itLC3N2+8+cPGTnbG8cMHb/wgyhpmOIuxgSgNo2AUjIJRMAqIAAAzoinwkxmoqQAAAABJRU5ErkJggg==","orcid":"","institution":"University Medical Center Hamburg-Eppendorf","correspondingAuthor":true,"prefix":"","firstName":"Tobias","middleName":"","lastName":"Dust","suffix":""},{"id":623843371,"identity":"f6117141-deeb-4f03-aaea-de6198cebcaa","order_by":1,"name":"Julian-Elias Henneberg","email":"","orcid":"","institution":"University Medical Center Hamburg-Eppendorf","correspondingAuthor":false,"prefix":"","firstName":"Julian-Elias","middleName":"","lastName":"Henneberg","suffix":""},{"id":623843373,"identity":"39406964-7fb3-401d-a741-2f37bb791fde","order_by":2,"name":"Maximilian Hartel","email":"","orcid":"","institution":"University Medical Center Hamburg-Eppendorf","correspondingAuthor":false,"prefix":"","firstName":"Maximilian","middleName":"","lastName":"Hartel","suffix":""},{"id":623843374,"identity":"dd493b51-6e14-4507-b8c3-140b3c900d2d","order_by":3,"name":"Alonja Reiter","email":"","orcid":"","institution":"University Medical Center Hamburg-Eppendorf","correspondingAuthor":false,"prefix":"","firstName":"Alonja","middleName":"","lastName":"Reiter","suffix":""},{"id":623843375,"identity":"9193d254-d309-4b58-a94e-7122848ad699","order_by":4,"name":"Julian Kylies","email":"","orcid":"","institution":"University Medical Center Hamburg-Eppendorf","correspondingAuthor":false,"prefix":"","firstName":"Julian","middleName":"","lastName":"Kylies","suffix":""},{"id":623843376,"identity":"40ada8eb-e828-4cba-812c-bbbc6cedce5b","order_by":5,"name":"Alexander Korthaus","email":"","orcid":"","institution":"University Medical Center Hamburg-Eppendorf","correspondingAuthor":false,"prefix":"","firstName":"Alexander","middleName":"","lastName":"Korthaus","suffix":""},{"id":623843377,"identity":"9f6559bf-9694-414f-af64-c1c549733a21","order_by":6,"name":"Anna Streckenbach","email":"","orcid":"","institution":"University Medical Center Hamburg-Eppendorf","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"Streckenbach","suffix":""},{"id":623843378,"identity":"8d3b5c2f-951a-410d-b048-13b8fe104a62","order_by":7,"name":"Johannes Keller","email":"","orcid":"","institution":"University Medical Center Hamburg-Eppendorf","correspondingAuthor":false,"prefix":"","firstName":"Johannes","middleName":"","lastName":"Keller","suffix":""},{"id":623843379,"identity":"168e026e-b7e1-4990-be97-7985c4f3ca89","order_by":8,"name":"Karl-Heinz Frosch","email":"","orcid":"","institution":"University Medical Center Hamburg-Eppendorf","correspondingAuthor":false,"prefix":"","firstName":"Karl-Heinz","middleName":"","lastName":"Frosch","suffix":""},{"id":623843380,"identity":"ccebd24e-2ba1-47e4-a6c0-655566fdfca8","order_by":9,"name":"Matthias Krause","email":"","orcid":"","institution":"University Medical Center Hamburg-Eppendorf","correspondingAuthor":false,"prefix":"","firstName":"Matthias","middleName":"","lastName":"Krause","suffix":""}],"badges":[],"createdAt":"2026-03-29 14:10:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9259392/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9259392/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107484853,"identity":"29b8a622-c0a8-424b-85c9-cb68ffc25d19","added_by":"auto","created_at":"2026-04-22 02:33:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":349278,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9259392/v1/c36044ec-b2e6-4d63-86f0-7d3fbebc693c.pdf"},{"id":107319461,"identity":"adde3d32-4cb1-45fc-99f0-526851b69eb3","added_by":"auto","created_at":"2026-04-20 10:16:22","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17201545,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-9259392/v1/290fd0bfe09f29e0ffbf49db.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mixed reality improves agreement on surgical approach selection and patient positioning in tibial plateau fracture planning compared to CT, 3DCT and 3D printing","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe surgical treatment of tibial plateau fractures remains one of the most demanding tasks in orthopaedic trauma surgery \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Due to the complex three-dimensional fracture morphology and frequent involvement of posterior and posterolateral segments, accurate preoperative planning is essential to achieve anatomical reduction, stable fixation, and appropriate functional outcomes \u003csup\u003e\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. In particular, the choice of treatment concept, patient positioning, and surgical approach plays a decisive role in enabling adequate exposure of the fracture site and avoiding intraoperative compromises that may negatively affect reduction quality and implant placement \u003csup\u003e\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eComputed tomography (CT) and three-dimensional computed tomography (3DCT) reconstructions have become standard tools for the assessment of tibial plateau fractures and substantially improved fracture visualization compared with conventional radiography \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Nevertheless, important aspects of spatial fracture understanding and fragment interrelation may remain difficult to appreciate using two-dimensional screens, especially in complex fracture patterns. To overcome these limitations, point-of-care three-dimensional (3D) printing has been introduced as an adjunctive technology for preoperative planning \u003csup\u003e\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Previous studies have demonstrated that physical 3D-printed fracture models can enhance the surgeon\u0026rsquo;s spatial understanding and may improve agreement regarding patient positioning and surgical approach selection, particularly among less experienced surgeons \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. However, 3D printing is associated with additional costs, production time, and logistical constraints, which may limit its routine clinical use \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMore recently, immersive visualization technologies such as virtual reality (VR) and mixed reality (MR) have gained increasing attention in orthopaedic trauma surgery \u003csup\u003e\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. These technologies allow interactive three-dimensional visualization of patient-specific fracture anatomy without the need for physical model production. While VR and MR have already been investigated for fracture diagnostics and classification tasks \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, evidence regarding their role in preoperative decision making remains limited. In particular, it is currently unclear whether MR can provide a comparable or even superior benefit to 3D printing when planning critical operative parameters such as treatment concept, patient positioning, and surgical approach.\u003c/p\u003e \u003cp\u003eTherefore, the purpose of this study was to assess the impact of mixed reality on preoperative planning of tibial plateau fractures in comparison with CT, 3DCT, and physical 3D-printed models. Specifically, we aimed to analyze interobserver agreement regarding (1) treatment concept, (2) patient positioning, and (3) surgical approach selection using these different visualization modalities. We hypothesized that mixed reality would improve agreement on key preoperative planning decisions compared with conventional imaging techniques and physical 3D-printed models, with potential differences across varying levels of surgical experience.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eThis study was designed as a retrospective observer study evaluating the impact of different visualisation modalities on preoperative planning of tibial plateau fractures. Conventional computed tomography (CT), three-dimensional computed tomography (3DCT), physical 3D-printed fracture models, and mixed reality (MR) visualisation were compared with regard to agreement on key preoperative planning decisions.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFracture cases\u003c/h3\u003e\n\u003cp\u003eA total of 22 intra-articular tibial plateau fractures (AO/OTA type B or C) treated between 2017 and 2022 were included. Eligibility required availability of a preoperative CT scan with an axial slice thickness of 1 mm or less and complete multiplanar reconstructions. Cases with previous fractures of the proximal tibia or incomplete imaging data were excluded. All datasets were fully anonymised prior to analysis.\u003c/p\u003e\n\u003ch3\u003eImaging data and three-dimensional reconstructions\u003c/h3\u003e\n\u003cp\u003eCT datasets were obtained from the institutional picture archiving and communication system (PACS) and stored in Digital Imaging and Communications in Medicine (DICOM) format. Raters reviewed axial, sagittal and coronal CT image stacks in a scrollable format simulating standard PACS navigation.\u003c/p\u003e \u003cp\u003eThree-dimensional CT reconstructions of the proximal tibia and fibula were generated from the same datasets. The femur and patella were digitally removed to improve visualisation of the tibial plateau and fracture morphology. The 3D reconstructions were presented as rotatable models allowing free inspection from different perspectives.\u003c/p\u003e\n\u003ch3\u003eSegmentation and 3D printing\u003c/h3\u003e\n\u003cp\u003eFracture segmentation was performed using Materialise Mimics Innovation Suite (version 24; Materialise, Leuven, Belgium) (Supplement 1). A threshold-based semi-automatic segmentation approach was applied to isolate osseous structures, followed by manual refinement in cases of comminution or depressed fragments. Post-processing was carried out using Materialise 3-matic Medical (version 16; Materialise, Leuven, Belgium) (Supplement 2, A \u0026amp; B), including surface smoothing and stabilisation of fracture fragments where required.\u003c/p\u003e \u003cp\u003eThe segmented fracture models were exported as standard tessellation language (STL) files and printed at a 1:1 scale using an Ultimaker S5 dual-head fused deposition modelling (FDM) printer (Ultimaker, Utrecht, The Netherlands) (Supplement 2, C \u0026amp; D). Polylactic acid (PLA) was used as the primary printing material, while water-soluble polyvinyl alcohol (PVA) served as support material. A layer thickness of 0.1 mm was selected to ensure high geometric accuracy and sufficient detail of the fracture morphology. After printing, support structures were removed prior to evaluation. The physical 3D-printed models were used exclusively for visual and tactile inspection during the respective study phase. No digital images or photographs of the printed models were provided to the raters.\u003c/p\u003e\n\u003ch3\u003eMixed reality visualisation\u003c/h3\u003e\n\u003cp\u003eMixed reality visualisation was performed using the Microsoft HoloLens 2 head-mounted display. Patient-specific 3D fracture models generated from the segmentation workflow were imported into the Materialise Viewer application (Supplement 3). Raters were able to interact with the virtual fracture models using standard MR gestures, including rotation, scaling, and free spatial positioning within the real-world environment. All raters received a standardised introduction to the MR system prior to the study.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eRaters\u003c/h2\u003e \u003cp\u003eTwelve orthopaedic trauma surgeons participated in the study, including six junior and six senior surgeons. All raters were blinded to the actual surgical treatment performed in the clinical cases.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy procedure\u003c/h3\u003e\n\u003cp\u003eEach rater evaluated all 22 fracture cases sequentially using four different visualisation modalities:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eCT,\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e3DCT,\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ephysical 3D-printed model, and\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003emixed reality visualisation.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThe order of the modalities was identical for all raters. At each stage, raters were asked to independently determine three preoperative planning parameters:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003etreatment concept,\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003epatient positioning, and\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003esurgical approach.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003ePredefined answer options were provided for each parameter to ensure standardised assessment across raters using an online questionnaire (Supplement 4). Multiple surgical approaches could be selected if a combined approach was considered necessary. Raters were not permitted to revise previous answers after progressing to the next stage.\u003c/p\u003e \u003cp\u003eOutcome measures\u003c/p\u003e \u003cp\u003eThe primary outcome measure was interobserver agreement for surgical approach selection. Secondary outcome measures included interobserver agreement for patient positioning and treatment concept selection.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eInterobserver agreement was analysed using Fleiss\u0026rsquo; kappa (κ) for each outcome parameter and visualisation modality. Kappa values were interpreted according to the criteria proposed by Landis and Koch (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In addition, percentage match (PM) was calculated as a descriptive measure of agreement, representing the proportion of identical ratings among raters.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLandis and Koch grading of reliability based on κ co-efficient values\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eκ co-efficient\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ereliability grading\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epoor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.01\u0026ndash;0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eslight\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.21\u0026ndash;0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efair\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.41\u0026ndash;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emoderate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.61\u0026ndash;0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esubstantial\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eexcellent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAgreement analyses were performed for the entire rater cohort as well as separately for junior and senior surgeons. Statistical analyses were conducted using SPSS Statistics (version 26; IBM Corp., Armonk, NY, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEthics approval\u003c/h2\u003e \u003cp\u003e The study was approved by the Ethics Committee of the Medical Chamber of Hamburg, Germany (ID: 2024-101279-BO-ff) and was conducted in accordance with the Declaration of Helsinki. All imaging data were anonymised prior to analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eInterobserver agreement for surgical approach selection\u003c/h2\u003e \u003cp\u003eInterobserver agreement for surgical approach selection, defined as the primary outcome parameter, is summarised in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Overall agreement was low to moderate across all visualisation modalities. Percentage match (PM) was 29% for both CT and 3DCT, increased to 33% for 3D-printed models, and reached 32% for mixed reality. Fleiss\u0026rsquo; kappa values showed a comparable pattern, ranging from 0.22 for 3DCT to 0.30 for mixed reality.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003einterobserver agreements for the surgical approach\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eSurgical approach\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCT\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cem\u003e3DCT\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003e3D\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cem\u003eMR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePM\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eκ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePM\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eκ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ePM\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eκ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ePM\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eκ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e32%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior Surgeons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e39%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSenior Surgeons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e31%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhen stratified by level of experience, junior surgeons demonstrated lower agreement across all modalities. In this subgroup, PM increased from 27% with CT and 3DCT to 34% with 3D-printed models and to 39% with mixed reality, while kappa values increased from 0.19 to 0.28. Among senior surgeons, agreement remained relatively stable across modalities, with PM values between 31% and 33% and kappa values between 0.21 and 0.27.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eInterobserver agreement for patient positioning\u003c/h2\u003e \u003cp\u003eInterobserver agreement for patient positioning is presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Across all raters, PM was 46% for both CT and 3DCT, increased to 53% with 3D-printed models, and further increased to 57% with mixed reality. Corresponding kappa values were 0.25 for CT, 0.26 for 3DCT, 0.36 for 3D printing, and 0.35 for mixed reality.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003einterobserver agreements for the patient positioning\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ePatient positioning\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCT\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cem\u003e3DCT\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003e3D\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cem\u003eMR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePM\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eκ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePM\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eκ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ePM\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eκ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ePM\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eκ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e53%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e57%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior Surgeons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e57%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSenior Surgeons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e55%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eJunior surgeons demonstrated a marked increase in agreement with advanced visualisation modalities. In this subgroup, PM increased from 42% with CT to 52% with 3D printing and to 57% with mixed reality, while kappa values increased from 0.19 to 0.39. In contrast, senior surgeons showed higher baseline agreement, with PM values ranging from 53% to 55% and kappa values between 0.27 and 0.36, without substantial differences between modalities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eInterobserver agreement for treatment concept\u003c/h2\u003e \u003cp\u003eInterobserver agreement for treatment concept selection is shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Agreement was high across all visualisation modalities. Overall percentage match (PM) values ranged from 82% to 91%, with the highest PM observed for mixed reality. Fleiss\u0026rsquo; kappa values were low to moderate and ranged from 0.17 to 0.25 across modalities.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003einterobserver agreements for the treatment concept\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eTreatment concept\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCT\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cem\u003e3DCT\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003e3D\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cem\u003eMR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePM\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eκ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePM\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eκ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ePM\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eκ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ePM\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eκ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e82%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e91%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior Surgeons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e81%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e91%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSenior Surgeons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e83%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e92%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSimilar patterns were observed in both junior and senior surgeons. Junior surgeons demonstrated PM values between 81% and 91%, whereas senior surgeons showed PM values between 83% and 92%. Kappa values remained within a comparable range across all modalities and experience levels.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe primary finding of this study is that Mixed Reality (MR) improved interobserver agreement in preoperative planning for tibial plateau fractures (TPF), particularly in the domains of surgical approach selection and patient positioning. While agreement on treatment concepts was already high across all modalities MR showed the clearest improvements in surgical approach selection and patient positioning\u0026mdash;two areas where interobserver agreement was lowest with conventional modalities. These benefits were most pronounced among less experienced raters, suggesting that MR offers a valuable cognitive support tool for surgical planning. Although we observed consistent trends favoring MR, the study was not powered to assess statistical significance.\u003c/p\u003e \u003cp\u003eCorrect patient positioning and surgical access are foundational to optimal outcomes in intra-articular fracture management. Mispositioning can compromise intraoperative visualization, increase operative time, and complicate access to key fracture zones \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan additionalcitationids=\"CR22 CR23\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. In tibial plateau fractures\u0026mdash;especially those involving posterior fragments\u0026mdash;optimal positioning (e.g., prone vs. supine, flexion angle) determines whether an anatomic reduction can be achieved without excessive soft tissue dissection \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eLikewise, choosing the correct surgical approach is critical for preserving soft tissue integrity, minimizing neurovascular injury, and enabling stable implant positioning \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Manidakis et al. further linked suboptimal treatment strategies to poor functional recovery and persistent postoperative pain \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Our findings support that MR, by enhancing spatial understanding, may reduce these risks through more accurate preoperative access planning. Although not measured directly in our study, this planning accuracy has the potential to translate into improved intraoperative efficiency and patient safety.\u003c/p\u003e \u003cp\u003e Our findings align with and expand upon earlier work on 3D visualization in orthopaedic trauma care. Prior studies have shown that 3D printing can improve diagnostic confidence, spatial understanding, and decision-making in managing intra-articular fractures \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. For instance, 3D printed models improved rater agreement in surgical approach and patient positioning decisions, especially among less experienced users \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Additional research on acetabular and pilon fractures supports similar trends \u003csup\u003e\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMR appears to extend these benefits. In contrast to the static inspection of physical models, MR offers interactive and dynamic fracture exploration, allowing users to manipulate holograms within the surgical field of view. Prior evidence for the preoperative utility of MR in orthopaedics remains limited. Bitschi et al. evaluated MR in tibial plateau fractures and found that visualization with MR changed the fracture segment classification in 79% of cases using the 10-segment system, suggesting that dynamic holographic exploration may impact decision-making more than conventional modalities \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn contrast, Brouwers et al. conducted a study on acetabular fractures comparing virtual reality (VR) models to 3D printing \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Their findings revealed that VR did not surpass 3D printing in terms of planning accuracy or user confidence, highlighting limitations in the standardization and usability of immersive technologies. These contrasting results underscore the need to interpret MR outcomes in the context of hardware and software variation. While Brouwers et al. did not find a clear advantage of immersive environments over 3D printing in terms of planning accuracy, our results suggest that MR may offer superior value in dynamic planning contexts. Additionally, earlier work has demonstrated improved diagnostic accuracy and user confidence with MR in fracture classification, which further supports our findings in the preoperative planning domain \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eConsistent with previous work, our results highlight that MR\u0026rsquo;s benefits are most pronounced among junior surgeons. This aligns with prior studys who found that 3D technologies enhanced spatial comprehension and improved decision-making in less experienced surgeons \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. MR's immersive environment seems to provide an additional layer of cognitive support for early-career users. Interestingly, this benefit may be linked to a reduction in mental workload, as previously demonstrated by Lu et al., who used NASA-TLX metrics to assess MR-supported planning. Surgeons reported improved communication, spatial clarity, and lower frustration during decision-making when using MR environments \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. These cognitive advantages may explain the particularly strong performance improvements in positioning and access planning observed in our junior subgroup.\u003c/p\u003e \u003cp\u003eSenior surgeons benefit less markedly, likely due to their pre-existing spatial visualization and procedural planning skills. However, the high level of agreement and subjective utility reported by experienced raters suggests that MR may still serve as a valuable validation tool, even for experts.\u003c/p\u003e \u003cp\u003eTo date, few comparative studies have evaluated 3D printing and MR specifically in the context of preoperative planning for tibial plateau fractures \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. While both technologies rely on similar upstream processes\u0026mdash;namely high-resolution CT imaging and standardized segmentation\u0026mdash;their downstream implementation differs considerably.\u003c/p\u003e \u003cp\u003eOnce segmentation is complete, MR models can be rendered and reviewed almost immediately, whereas 3D printing typically requires 6\u0026ndash;8 hours of additional production time, depending on printer type and materials. This makes MR not only faster to implement but also less resource-intensive in acute or semi-urgent scenarios.\u003c/p\u003e \u003cp\u003eIn addition, MR environments such as those deployed with HoloLens 2 can be operated in sterile surgical settings due to their gesture-based interface and wearable design. In contrast, 3D printed models raise regulatory concerns regarding sterilization and OR compatibility, particularly when intended for intraoperative use.\u003c/p\u003e \u003cp\u003eThese practical differences highlight MR\u0026rsquo;s potential as a more flexible and scalable tool in both planning and intraoperative scenarios. Nevertheless, 3D printing continues to offer advantages in haptic feedback and interprofessional communication, particularly during surgical team discussions or patient consultations.\u003c/p\u003e \u003cp\u003eLimitations\u003c/p\u003e \u003cp\u003eOur study has several limitations. First, the CT scans were acquired at multiple institutions, and although all included datasets met the minimum resolution threshold for segmentation, variations in scan quality and protocol may have introduced heterogeneity in 3D model fidelity. However, to ensure consistency in model geometry, all DICOM datasets were processed using a standardised segmentation protocol and STL export workflow. Second, participant fatigue may have influenced scoring accuracy, as the rating process involved detailed review of 22 cases across four modalities. Although breaks were permitted and the sequence of cases randomized, cognitive fatigue cannot be fully excluded. Third, no formal training module was implemented prior to MR assessment. While participants were given brief instruction, variability in MR familiarity may have influenced performance\u0026mdash;particularly among less experienced raters. To reduce this bias, the fractures were presented to all raters in a random order.\u003c/p\u003e \u003cp\u003eFourth, this was an interobserver reliability study and did not assess intrarater reproducibility or direct clinical outcomes such as intraoperative times, quality of reduction, or complication rates. However, the inclusion of a large and heterogeneous group of raters across all experience levels, combined with a standardized and blinded assessment protocol, strengthens the generalizability and robustness of the observed agreement patterns.\u003c/p\u003e \u003cp\u003eFinally, variations in the technical capabilities of extended reality platforms (e.g., field of view, model resolution) complicate direct comparisons across studies. Standardization in MR model preparation and rendering remains an important prerequisite for multicenter validation. Despite these limitations, the use of high-quality segmentation protocols, multiple raters of varying experience, and comparison across four modalities provides a robust foundation for interpreting the observed improvements in agreement and planning confidence.\u003c/p\u003e \u003cp\u003eOutlook\u003c/p\u003e \u003cp\u003eFuture research should focus on the technical and clinical standardization of MR-assisted planning in orthopaedic trauma care. To enable comparability across studies, consensus is needed on hardware specifications, file formats, rendering quality, and segmentation protocols for extended reality applications. Comparative studies that benchmark various MR platforms and software environments will be essential to define technical benchmarks and usability standards. Furthermore, it is essential to define and investigate meaningful clinical outcome parameters that are currently lacking in most MR studies. Multicenter, prospective trials should integrate MR with intraoperative navigation systems to evaluate workflow compatibility and to explore potential synergy between technologies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eMixed Reality (MR) enhances interobserver agreement in key domains of preoperative planning for tibial plateau fractures, particularly in surgical approach selection and patient positioning. These benefits were most notable among less experienced surgeons, underscoring the value of MR as a cognitive support tool. Compared to conventional imaging and 3D printing, MR offers dynamic interaction, greater flexibility, and workflow efficiency. While further clinical studies are required to validate its intraoperative and patient-centered benefits, our findings support the integration of MR into modern orthopaedic trauma planning as a valuable adjunct across experience levels.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthor contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll authors drafted the manuscript. T.D., J.H., A.K., and J.K. were responsible for patient selection and 3D printing preparation. T.D., J.H., M.K., and M.H. designed the questionnaire and online survey tool. T.D., J.H., and A.R. performed data collection and statistics. T.D., J.H., and A.S. designed the figures. M.K., K.F., and J.K. revised the manuscript. K.F., M.K., and A.S. provided the theoretical background. All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for conducting this study.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e \u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting Interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e \u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData availability statement\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available due to institutional data protection policies but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\n\u003cp\u003e\u003cem\u003eEthikal approval\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of the Medical Chamber of Hamburg, Germany (ID: 2024-101279-BO-ff) and conducted in accordance with the Declaration of Helsinki. All participants provided written informed consent prior to their participation in the study.\u003c/p\u003e\n\n\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRaschke MJ, Herbst E. Tibial plateau fractures: a lot more to come! Eur J Trauma Emerg Surg. 2020;46:1201\u0026ndash;2. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00068-020-01551-6\u003c/span\u003e\u003cspan address=\"10.1007/s00068-020-01551-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeulenkamp B, et al. Risk Factors, and Location of Articular Malreductions of the Tibial Plateau. J Orthop Trauma. 2017;31:146\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/BOT.0000000000000735\u003c/span\u003e\u003cspan address=\"10.1097/BOT.0000000000000735\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Incidence.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Dreumel RL, van Wunnik BP, Janssen L, Simons PC, Janzing HM. Mid- to long-term functional outcome after open reduction and internal fixation of tibial plateau fractures. Injury. 2015;46:1608\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.injury.2015.05.035\u003c/span\u003e\u003cspan address=\"10.1016/j.injury.2015.05.035\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHonkonen SE. Degenerative arthritis after tibial plateau fractures. J Orthop Trauma. 1995;9:273\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/00005131-199509040-00001\u003c/span\u003e\u003cspan address=\"10.1097/00005131-199509040-00001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrause M, et al. Intra-articular tibial plateau fracture characteristics according to the Ten segment classification. Injury. 2016;47:2551\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.injury.2016.09.014\u003c/span\u003e\u003cspan address=\"10.1016/j.injury.2016.09.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEgol KA, Tejwani NC, Capla EL, Wolinsky PL, Koval KJ. Staged management of high-energy proximal tibia fractures (OTA types 41): the results of a prospective, standardized protocol. J Orthop Trauma. 2005;19:448\u0026ndash;55. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/01.bot.0000171881.11205.80\u003c/span\u003e\u003cspan address=\"10.1097/01.bot.0000171881.11205.80\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. discussion 456.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCho JW, et al. Approaches and fixation of the posterolateral fracture fragment in tibial plateau fractures: a review with an emphasis on rim plating via modified anterolateral approach. Int Orthop. 2017;41:1887\u0026ndash;97. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00264-017-3563-6\u003c/span\u003e\u003cspan address=\"10.1007/s00264-017-3563-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrapiriyakul W, Apivatthakakul T, Phornphutkul C. Posterolateral tibial plateau fractures, how to buttress? Reversed L posteromedial or the posterolateral approach: a comparative cadaveric study. Arch Orthop Trauma Surg. 2018;138:505\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00402-018-2875-3\u003c/span\u003e\u003cspan address=\"10.1007/s00402-018-2875-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlessio-Mazzola M, Placella G, Conca M, Salini V. Posterolateral tibia plateau fractures: pros and cons of different surgical approaches. EFORT Open Rev. 2025;10:416\u0026ndash;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1530/eor-2025-0037\u003c/span\u003e\u003cspan address=\"10.1530/eor-2025-0037\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCastiglia MT, et al. The Impact of Computed Tomography on Decision Making in Tibial Plateau Fractures. J Knee Surg. 2018;31:1007\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1055/s-0038-1627464\u003c/span\u003e\u003cspan address=\"10.1055/s-0038-1627464\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDust T, et al. The influence of 3D printing on inter- and intrarater reliability on the classification of tibial plateau fractures. Eur J Trauma Emerg Surg. 2022. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00068-022-02055-1\u003c/span\u003e\u003cspan address=\"10.1007/s00068-022-02055-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDust T, et al. 3D printing improves preoperative decision making for patient positioning and surgical approach selection for tibial plateau fractures. Sci Rep. 2025;15:26513. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-025-11995-8\u003c/span\u003e\u003cspan address=\"10.1038/s41598-025-11995-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAssink N, et al. Does 3D-assisted surgery of tibial plateau fractures improve surgical and patient outcome? A systematic review of 1074 patients. Eur J Trauma Emerg Surg. 2022;48:1737\u0026ndash;49. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00068-021-01773-2\u003c/span\u003e\u003cspan address=\"10.1007/s00068-021-01773-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGiannetti S, Bizzotto N, Stancati A, Santucci A. Minimally invasive fixation in tibial plateau fractures using an pre-operative and intra-operative real size 3D printing. Injury. 2017;48:784\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.injury.2016.11.015\u003c/span\u003e\u003cspan address=\"10.1016/j.injury.2016.11.015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuitema JM, et al. Are 3D-printed Models of Tibial Plateau Fractures a Useful Addition to Understanding Fractures for Junior Surgeons? Clin Orthop Relat Res. 2022;480:1170\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/CORR.0000000000002137\u003c/span\u003e\u003cspan address=\"10.1097/CORR.0000000000002137\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBallard DH, et al. Medical 3D Printing Cost-Savings in Orthopedic and Maxillofacial Surgery: Cost Analysis of Operating Room Time Saved with 3D Printed Anatomic Models and Surgical Guides. Acad Radiol. 2020;27:1103\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.acra.2019.08.011\u003c/span\u003e\u003cspan address=\"10.1016/j.acra.2019.08.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrouwers L, et al. What is the value of 3D virtual reality in understanding acetabular fractures? Eur J Orthop Surg Traumatol. 2020;30:109\u0026ndash;16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00590-019-02537-w\u003c/span\u003e\u003cspan address=\"10.1007/s00590-019-02537-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBitschi D, et al. Preoperative Mixed-Reality Visualization of Complex Tibial Plateau Fractures and Its Benefit Compared to CT and 3D Printing. J Clin Med. 2023;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/jcm12051785\u003c/span\u003e\u003cspan address=\"10.3390/jcm12051785\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDust T, et al. Mixed reality enhances interobserver reliability in tibial plateau fracture classification compared to computed tomography, 3D computed tomography and 3D printing. J Experimental Orthop. 2025;12:e70474. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/jeo2.70474\u003c/span\u003e\u003cspan address=\"10.1002/jeo2.70474\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu L, et al. Applications of Mixed Reality Technology in Orthopedics Surgery: A Pilot Study. Front Bioeng Biotechnol. 2022;10:740507. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fbioe.2022.740507\u003c/span\u003e\u003cspan address=\"10.3389/fbioe.2022.740507\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrause M, Kr\u0026uuml;ger S, M\u0026uuml;ller G, P\u0026uuml;schel K, Frosch KH. How can the articular surface of the tibial plateau be best exposed? A comparison of specific surgical approaches. Arch Orthop Trauma Surg. 2019. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00402-019-03200-z\u003c/span\u003e\u003cspan address=\"10.1007/s00402-019-03200-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGanguli A, et al. 3D printing for preoperative planning and surgical training: a review. Biomed Microdevices. 2018;20:65. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10544-018-0301-9\u003c/span\u003e\u003cspan address=\"10.1007/s10544-018-0301-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBear J, Diamond O, Helfet D. Strategies for Success in Plating of Complex Proximal Tibia Fractures. Oper Tech Orthop. 2018;28:157\u0026ndash;63. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1053/j.oto.2018.07.006\u003c/span\u003e\u003cspan address=\"10.1053/j.oto.2018.07.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNie W, et al. Preliminary application of three-dimension printing technology in surgical management of bicondylar tibial plateau fractures. Injury. 2019;50:476\u0026ndash;83. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.injury.2018.12.019\u003c/span\u003e\u003cspan address=\"10.1016/j.injury.2018.12.019\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrause M, Frings J, Isik H, Frosch KH. Comparison of extended lateral approaches to the tibial plateau: The articular exposure of lateral epicondyle osteotomy with and without popliteus tendon vs. fibula osteotomy. Injury. 2020;51:1874\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.injury.2020.05.038\u003c/span\u003e\u003cspan address=\"10.1016/j.injury.2020.05.038\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoluda-Mengod J, et al. A proposal of a new algorithm for decision-making approaches in open reduction and internal fixation of complex tibial plateau fractures - SOTA algorithm (Spanish Orthopaedic Trauma Association). Injury. 2021;52(Suppl 4):S87\u0026ndash;98. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.injury.2021.03.064\u003c/span\u003e\u003cspan address=\"10.1016/j.injury.2021.03.064\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManidakis N, et al. Tibial plateau fractures: functional outcome and incidence of osteoarthritis in 125 cases. Int Orthop. 2010;34:565\u0026ndash;70. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00264-009-0790-5\u003c/span\u003e\u003cspan address=\"10.1007/s00264-009-0790-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrouwers L, et al. The Value of 3D Printed Models in Understanding Acetabular Fractures. 3D Print Additive Manuf. 2018;5:37\u0026ndash;46. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1089/3dp.2017.0043\u003c/span\u003e\u003cspan address=\"10.1089/3dp.2017.0043\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePhelps KD, et al. A Randomized Trial of Preoperative Practice in a Simulated Operative Environment: 3D Printed Pilon Model Improves Resident Performance. J Surg Orthop Adv. 2022;31:187\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBai J, et al. Efficacy and safety of 3D print-assisted surgery for the treatment of pilon fractures: a meta-analysis of randomized controlled trials. J Orthop Surg Res. 2018;13:283. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13018-018-0976-x\u003c/span\u003e\u003cspan address=\"10.1186/s13018-018-0976-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu AM, et al. The addition of 3D printed models to enhance the teaching and learning of bone spatial anatomy and fractures for undergraduate students: a randomized controlled study. Ann Transl Med. 2018;6:403. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.21037/atm.2018.09.59\u003c/span\u003e\u003cspan address=\"10.21037/atm.2018.09.59\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eColcuc C, et al. Comparison of virtual reality and computed tomography in the preoperative planning of complex tibial plateau fractures. Arch Orthop Trauma Surg. 2024;144:2631\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00402-024-05348-9\u003c/span\u003e\u003cspan address=\"10.1007/s00402-024-05348-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLandis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977;33:159\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-trauma-and-emergency-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejot","sideBox":"Learn more about [European Journal of Trauma and Emergency Surgery](http://link.springer.com/journal/68)","snPcode":"68","submissionUrl":"https://submission.nature.com/new-submission/68/3","title":"European Journal of Trauma and Emergency Surgery","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"tibial plateau fracture, mixed reality, preoperative planning, patient positioning, surgical approach, interobserver agreement","lastPublishedDoi":"10.21203/rs.3.rs-9259392/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9259392/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eTibial plateau fractures require precise preoperative planning, particularly regarding treatment concept, patient positioning, and surgical approach. Mixed reality (MR) may enhance three-dimensional understanding of fracture morphology beyond conventional imaging and physical 3D models. This study investigated whether MR improves agreement on key preoperative planning decisions compared to computed tomography (CT), 3D computed tomography (3DCT), and 3D-printed fracture models.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eTwelve orthopaedic trauma surgeons (6 junior, 6 senior) evaluated 22 surgically treated tibial plateau fractures (AO/OTA type B or C). Each case was assessed in four steps using (1) CT, (2) 3DCT reconstructions, (3) physical 3D-printed models, and (4) MR visualization using Microsoft Hololens 2. Raters selected (i) treatment concept, (ii) patient positioning, and (iii) surgical approach. Interobserver agreement was analysed using Fleiss\u0026rsquo; kappa (κ) and percentage match (PM).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMixed Reality (MR) yielded the highest interobserver agreement for both surgical approach (PM 32%, κ\u0026thinsp;=\u0026thinsp;0.30) and patient positioning (PM 57%, κ\u0026thinsp;=\u0026thinsp;0.35), particularly among junior surgeons (approach PM 39%, κ\u0026thinsp;=\u0026thinsp;0.28; positioning PM 57%, κ\u0026thinsp;=\u0026thinsp;0.39). Compared to CT, 3DCT, and 3D printing, MR demonstrated consistent improvements, while agreement on treatment concepts remained high across all modalities (PM\u0026thinsp;\u0026gt;\u0026thinsp;82%).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eMixed Reality (MR) improved interobserver agreement in preoperative planning for tibial plateau fractures, particularly for surgical approach selection and patient positioning. These effects were most pronounced among less experienced raters, highlighting MR\u0026rsquo;s role as a cognitive support tool and a promising instrument in surgical education. Compared to conventional imaging and 3D printing, MR provided dynamic interaction and greater planning flexibility. While further clinical validation is required, these findings support MR as a valuable adjunct in orthopaedic trauma planning.\u003c/p\u003e\u003ch2\u003eLevel of evidence:\u003c/h2\u003e \u003cp\u003eLevel II\u003c/p\u003e","manuscriptTitle":"Mixed reality improves agreement on surgical approach selection and patient positioning in tibial plateau fracture planning compared to CT, 3DCT and 3D printing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-20 10:16:13","doi":"10.21203/rs.3.rs-9259392/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-25T18:49:11+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-25T17:43:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-22T03:28:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"107349373252755257659258107173100018154","date":"2026-04-15T18:42:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"105640746445570628016163879160062487317","date":"2026-04-14T04:55:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-13T12:16:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-12T14:03:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-02T12:26:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Trauma and Emergency Surgery","date":"2026-03-29T13:59:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-trauma-and-emergency-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejot","sideBox":"Learn more about [European Journal of Trauma and Emergency Surgery](http://link.springer.com/journal/68)","snPcode":"68","submissionUrl":"https://submission.nature.com/new-submission/68/3","title":"European Journal of Trauma and Emergency Surgery","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e4cca4dc-07e9-41af-9f9e-714f7cf37b03","owner":[],"postedDate":"April 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-09T08:23:46+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-20 10:16:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9259392","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9259392","identity":"rs-9259392","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}