Sage Journals: Discover world-class research

Abstract

Objective

The management of complex tibial plateau fractures remains a challenge in trauma orthopedics. Our team developed the Zhejiang University Surgical Simulation Digital Platform. This study evaluated the clinical utility of this intelligent assistance system in treating complex tibial plateau fractures and summarizes our experience and outcomes.

Methods

Patients with complex tibial plateau fractures (Schatzker types IV–VI) were randomly assigned to two groups: Group A, using the digital simulation platform; and Group B, conventional planning. All patients underwent preoperative X-ray and computed tomography (CT) imaging to formulate surgical strategies. Outcomes were compared, including operating time, fracture healing time, postoperative Rasmussen knee scores, knee range of motion (ROM), fracture reduction quality, and complications.

Results

From January 2022 to July 2023, 28 patients (Group A, n = 14; Group B, n = 14; age, 19–72 years) were enrolled, with follow-up exceeding 12 months. Operating time was significantly shorter in Group A (108 vs. 129 minutes, P < 0.05). Group A had better postoperative Rasmussen scores (26.7 vs. 25.0) and knee ROM (124° vs. 116°), although the differences were not significant (P ＞ 0.05). The excellent fracture reduction rate was higher in Group A (86% vs. 71%, P ＞ 0.05).

Conclusion

The digital surgical simulation platform was useful for treating complex tibial plateau fractures. It facilitated precise preoperative planning, enhanced surgeons’ spatial understanding of fracture morphology, and enabled the simulation of surgical maneuvers. Its application significantly reduces operating time, improves fracture reduction accuracy, and optimizes postoperative knee function.

Keywords

Complex tibial plateau fracture digital surgical simulation platform knee function fracture reduction preoperative planning

Introduction

Complex tibial plateau fractures present formidable challenges in trauma orthopedics, often leading to malunion, restricted joint mobility, and complication rates of 3–20%^1–3; those with severe symptoms often require joint replacement at a later date. Therefore, improving the treatment of complex tibial plateau fractures is an important goal in orthopedics. Traditional preoperative planning relies on two-dimensional (2D) imaging [X-rays, computed tomography (CT), and magnetic resonance imaging (MRI)] and surgeon experience, which may inadequately represent the three-dimensional (3D) anatomy and compromise surgical outcomes. Two-dimensional images cannot visually display the state of bone in 3D space, which causes difficulties in diagnosis and surgical treatment, which may lead to poor surgical results, prolonged operating times, increased risk of infection, and other problems. In preoperative communication with patients, the expected surgical process cannot be shown to patients visually, and an individualized display plan cannot be provided, which increases the difficulty in doctor–patient communication. Such fractures are often a challenge, even for experienced orthopedic surgeons. Recent advances in computer-assisted technologies, including 3D reconstruction, artificial intelligence (AI), and augmented reality (AR), have revolutionized orthopedic practice.^4–6 These innovations enable personalized preoperative simulations, addressing the limitations of conventional methods.

Many recent studies have reported the application of preoperative simulated surgical platforms in orthopedic surgery. For example, in the United States, Hsu et al. began to use OrthoView software for preoperative planning of knee replacement surgery in 2012.⁷ Based on postoperative results, the software was used to predict the patient's implant size with 83% accuracy in the femur and 90% accuracy in the tibia. In 2016, Jentzsch et al. at the University of Zurich used Mimics software to reconstruct bone tumor 3D models based on MRI images and bone and muscle tissue 3D models based on CT data, and conducted 3D registration of the models using computer-assisted surgery planning application software preoperatively to assist doctors in determining the optimal bone tumor resection plane.⁸ Compared with the traditional method of bone tumor resection based on the experience of clinicians, their method obtained more accurate bone tumor resection planes, reduced the destruction of other tissues, and improved the surgical outcome. There are related reports in orthopedics trauma. For example, it has been applied in periacetabular osteotomy,⁹ pedicle screw placement in spinal surgery,¹⁰ and the treatment of distal femoral¹¹ and acetabular fractures^12,13 with good results. However, there are relatively few digital studies of the preoperative planning of complex tibial plateau fractures. We independently developed an orthopedic operation simulation digital management system, applied it to the treatment of tibial plateau fractures, and summarized our experience and relevant clinical results.

Materials and methods

Patient enrollment

This study was approved by the Ethics Committee of The Second Affiliated Hospital, Zhejiang University School of Medicine (IRB approval number: IRB-2024–1394). Written informed consent was obtained from all participants prior to the initiation of the study at their respective institutions. This study was conducted in accordance with the principles of the Declaration of Helsinki. This is a prospective randomized study conducted at the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China. This study included patients with complex tibial plateau fractures (Schatzker IV–VI patients) admitted to our hospital between January 2022 and July 2023. The patients were randomly divided into two groups in a 1:1 ratio. The surgical simulation digital platform was used in group A, but not in group B. The inclusion criteria were as follows: (1) Schatzker type IV–VI patients; (2) time from injury to surgery < 3 weeks; (3) patients followed for > 1 year; (4) patients > 18 years old. The exclusion criteria were as follows: (1) comorbid mental illness or other cognitive disorders likely to compromise postoperative functional assessment; (2) pathological fractures; (3) open tibial plateau fractures.

The surgical simulation operating system used in this study was a trauma orthopedic surgery simulation platform jointly developed by a team at the School of Computer Science of Zhejiang University and the clinical department where the author works. The system functions include 3D bone reconstruction, fracture segmentation, fracture simulation splicing, and consumable simulation control. It has not yet been commercialized and is only used internally.

Randomization

After enrollment, patients were randomly assigned in a 1:1 ratio to either the digital simulation platform group or the conventional planning group. The randomization sequence was generated by a biostatistician using a computer random number generator (IBM SPSS Statistics for Windows, Version 26.0). Allocation concealment was ensured through the sequentially numbered, opaque, sealed envelopes (SNOSE). Each envelope contained the group allocation and was opened by the attending surgeon only after the patient had provided written informed consent and was prepared for surgery.

Preoperative planning

Patients were admitted to hospital after a definite diagnosis, and the affected limb was subjected to external plaster fixation or bone traction of the calcaneal tubercle. Routine X-ray and CT examinations were performed, and some patients underwent MRI; surgery was performed after the swelling of the affected limb subsided. The fracture characteristics of patients were determined in a preoperative examination; the mechanism of fracture injury was determined; and the surgical approach and fixation method were established according to the fracture characteristics. In addition to traditional imaging examinations, a thin-slice CT image file in Digital Imaging and Communications in Medicine (DICOM) format was uploaded for patients in group A to generate 3D fracture images in our preoperative surgical simulation operating platform. The system automatically segments and stains the bone blocks, hides, moves, and rotates the fracture blocks to understand the fracture, and performs pre-operations in the system to determine the fracture reduction sequence and achievable reduction (Figure 1).

Figure 1.

Digital surgical simulation platform.

Description of the digital surgical simulation platform

The segment anything model (SAM), trained on large-scale data, possesses strong generalization capabilities and can automatically identify and segment various objects in images. Through pre-training, SAM learns rich feature representations from extensive image datasets, enabling it to perform exceptionally well across a range of image segmentation tasks. In CT image segmentation, precise prompt design can guide SAM to focus on specific anatomical structures or pathological regions, helping the model accurately localize and segment fracture areas within complex CT images.

Additionally, incorporating methods such as attention mechanisms can address certain challenges unique to medical imaging. The attention mechanism is a deep learning approach designed to improve model performance. Its core idea is to assign different weights to different parts of the input data, allowing the model to more effectively capture important information. In fracture segmentation, introducing attention mechanisms can enhance the model's ability to accurately segment fractures within complex fissures. By applying attention, the model can more precisely focus on key fracture regions in CT images while ignoring irrelevant background information. Moreover, fracture lines exhibit spatial characteristics—subtle cracks in certain slices may be more apparent in adjacent slices. Attention mechanisms can leverage this spatial property to extract information that other models might miss. Integrating attention mechanisms not only improves segmentation accuracy but also enhances the model's generalization capability across diverse fracture morphologies.

Surgeon and implants

All surgeries were performed by two senior orthopedic surgeons, each with over 10 years of subspecialty experience in knee trauma and reconstructive surgery. Both surgeons are fellowship-trained in orthopedic traumatology and collectively perform >60 tibial plateau fracture fixations annually at our Level I trauma center. The surgical team was consistent throughout the study period, and no trainee or general orthopedist served as the primary surgeon.

All patients received Zimmer Biomet periarticular plating systems. The Zimmer Periarticular Proximal Tibial Locking Plate was used. No alternative manufacturers or non-locking implants were used.

Surgical technique

The patients were placed on a fluoroscope operating bed on their backs, general anesthesia was administered, a tourniquet was applied on the affected limb, and medial and lateral incisions were routinely used for fracture reduction and treatment.

For medial plateau fractures, the medial longitudinal incision of the knee joint was about 10 cm long. The skin and subcutaneous and deep fascia were incised successively to protect the medial collateral ligament, and the pes anserinus was removed subperiosteally. If the articular surface of the medial platform did not collapse or split, the articular capsule was not cut. The metaphyseal of the medial platform was reduced, and a Kirschner wire was placed temporarily. If the articular surface of the medial plateau collapsed and split, the articular capsule was cut transversely, and the meniscus was pulled to the proximal end to reveal the articular surface of the medial plateau of the tibia. The fracture end was cleaned; the articular surface bone mass was reduced; and a Kirschner wire was temporarily fixed. The medial platform was fixed with an anatomic locking plate or reconstruction plate after satisfactory reduction of the fracture using a C-arm X-ray machine.

For lateral platform fractures, the affected limb was placed in mild flexion, and a 10–15-cm-long curved incision was made centered on Gerdy's tubercle. After the skin incision, the iliotibial band was split in the middle along the fiber direction, and sharp dissection was made toward the front and back of Gerdy's tubercle. Most of the lateral platforms had collapsed articular facet bone, and the articular capsule was cut across. The lateral meniscus marginal sutures were left for traction and raised to the proximal end to expose the articular surface of the lateral tibial plateau. The fracture end was cleaned; the articular surface bone was reduced under direct vision; the bone defect was supported by bone grafting; and a Kirschner needle was temporarily fixed. Anatomical locking plates were used to fix the lateral platform after satisfactory fracture reduction monitored with a C-arm X-ray machine.

After all repairs were completed, the knee joint stability was rechecked using the front and back drawer tests, the knee joint valvaration stress test at 0° and 30°, and the dial test.

Postoperative management

Routine antibiotics were used for 2 days postoperatively to prevent infection, and analgesics, anticoagulants, and other treatments were given. Passive joint functional activities began on the first postoperative day, with bed rest for 2 weeks postoperatively and functional exercises in bed. After 2 weeks, weight-bearing training of the lower limbs was gradually implemented under the guidance of doctors.

Weight-bearing advancement followed a fixed, criterion-based protocol rather than a fixed time-based schedule. Progression was permitted only when the following criteria were met:

Phase I (Weeks 0–2): Non-weight-bearing (NWB)

The operated limb remained non-weight-bearing. Bed rest with limb elevation was encouraged. Ankle pumps and quadriceps isometric contractions were initiated on postoperative day 2.

Phase II (Weeks 2–6): Toe-touch weight-bearing (TTWB, ≤20% body weight)

Patients were permitted to place the foot on the ground for balance only, without bearing significant weight. Advancement required: (1) well-healed surgical wound, no signs of infection; (2) active quadriceps strength ≥3/5 on manual muscle testing.

Phase III (Weeks 6–12): Partial weight-bearing (PWB, 50% body weight)

Patients progressed to 50% weight bearing using a bathroom scale for feedback training. Advancement required: (1) radiographic evidence of progressive callus formation on plain radiographs; (2) no implant failure or loss of reduction.

Phase IV (Week 12 onward): Progressive weight-bearing

Weight bearing was increased by 10% of body weight per week, guided by patient tolerance and gait quality. Full weight-bearing (FWB) was permitted only when all of the following criteria were satisfied: (1) Independent ambulation without crutches or walker; (2) painless weight acceptance during the stance phase; (3) radiographic confirmation of bridging callus on at least three cortices on orthogonal radiographs; and (4) no evidence of implant loosening or articular collapse.

Follow-up and outcomes

Regular reviews were performed 1, 2, 3, 6, 9, and 12 months after surgery. Anterolateral X-rays of the knee joints were obtained each time. Full-length lower extremity radiographs and CT of the knee joints were performed when necessary. Operating time, intraoperative blood loss, fracture healing time, fracture reduction, knee function score, knee motion, and postoperative complications were compared between the two groups.

Fracture healing was defined radiographically as bridging callus visible on at least three cortices on anteroposterior and lateral radiographs. Radiographs were obtained at each follow-up time point and independently reviewed by two musculoskeletal radiologists. Disagreements were resolved by consensus. Articular reduction was evaluated on 12-month CT using the Rasmussen radiological score. In addition, maximum articular step-off and gap distance (mm) were measured on coronal and sagittal reconstructions. All CT measurements were performed by an independent orthopedic surgeon who was not part of the surgical team and was blinded to group assignment.

Functional status was assessed using the Rasmussen functional score at 12 months postoperatively. Active knee range of motion (flexion/extension) was measured using a standard goniometer with the patient supine at 12 months. Assessments were conducted by a senior physiotherapist, who was unaware of the patient's allocation.

Complications were prospectively recorded using predefined diagnostic criteria: (1) Surgical site infection: CDC criteria (superficial/deep); (2) implant failure: screw breakage, plate bending, or loss of reduction requiring revision; (3) non-union: absence of bridging callus at 9 months; (4) deep vein thrombosis: duplex ultrasound-confirmed; and (5) others: nerve injury, compartment syndrome. Adjudication of all suspected complications was performed by an independent safety monitor, who is orthopedic surgeon not affiliated with the study.

Statistical analysis

For continuous variables, independent t-tests were used to compare fracture healing time, fracture reduction, knee function scores, and knee range of motion between groups. For categorical variables, Fisher's exact test was used to analyze fracture reduction evaluation and complications. A two-sided α < 0.05 was considered statistically significant. All analyses were performed on an intention-to-treat basis using SPSS version 26.0 (IBM Corp., Armonk, NY, USA).

Results

Baseline characteristics

This study included 28 patients with complex tibial plateau fractures aged 19–72 years, with 14 patients in each group. The follow-up times exceeded 12 months. The operating time was 108 minutes in group A and 129 minutes in group B (P < 0.05). Injuries resulted from car accidents (14 cases), falls (11 cases), and heavy objects (three cases). According to the Schatzker classification of tibial plateau fractures, 8, 13, and 7 cases were types IV–VI, respectively. Fifteen cases involved the medial + lateral columns, while 13 cases were tri-columnar. There were no significant differences between the two groups (Table 1).

Table 1.

Baseline characteristics of patients

	Group A	Group B	Statistics analysis
Cases Schatzker Classification IV V VI Three column classification Medial + lateral Medial + lateral + posterior Operation time (min) Bone healing time (month) Knee Rasmussen score Knee range motion Fracture reduction evaluation Excellent Complication	14 4 6 4 7 7 108 3.2 26.7 124° 12(86%) 1	14 4 7 3 8 6 129 3.5 25.0 116° 10 (71%) 1	P = 0.001 P = 0.123 P = 0.054 P = 0.051 P = 0.648 P = 1

Clinical outcomes

Bone healing was achieved in all patients at the last follow-up. The average healing time was 3.2 months in group A and 3.5 months in group B (P > 0.05). The average Rasmussen score was 26.7 in group A and 25.0 in group B; the average postoperative knee joint motion was 124° in group A and 116° in group B. The Rasmussen score and knee motion were better in group A (P > 0.05). The rate of fracture reduction was 86% in group A and 71% in group B (P > 0.05). Superficial wound infection occurred in one patient in each group, and no serious complications occurred (Figures 2 and 3).

Figure 2.

A 61-year-old female with a V type platform fracture, showing obvious fracture displacement, articular surface collapse, and knee subluxation. Using the surgical simulation operating platform, it was determined that anatomical reduction could be achieved. The fracture was fixed with medial and lateral double incision reduction. The postoperative examination indicated anatomic reduction, and the lower limb force line was normal.

Figure 3.

A 35-year-old man with a type VI platform fracture, comminuted articular surface, and partial knee dislocation. Using the surgical simulation operating platform, the fracture was fixed with medial and lateral double incision reduction, and the postoperative examination indicated anatomical reduction of the fracture.

Discussion

The development of AI technologies, such as computer vision, pattern recognition, and visual language large models, is empowering orthopedic surgery diagnosis and treatment. Intelligent orthopedics involves research on multiple technologies, such as orthopedic 3D reconstruction technology, additive manufacturing technology (3D printing), computer AR, orthopedic AI technology, computer-aided design (CAD), computer-aided manufacturing (CAM), computer-aided orthopedic navigation technology (CAOS), and orthopedic robotics technology.^14–16 In trauma orthopedics, 3D printing technology is most commonly used, while other research directions are still in the initial stages. Our research group has also used 3D printing technology in many fracture patients, with good results.¹⁷ However, 3D printing technology also has some shortcomings: it can take 5–7 days to print prostheses, extending the treatment time; it costs thousands of yuan; and while it can be used to observe the external conditions of bone, it cannot observe small changes inside a bone or be used to perform pre-surgery, and bone block movement on the printed prosthesis is inconvenient. Our surgical simulation operating platform solves these problems. Through computer simulation, it can generate corresponding images, realize measures such as an osteotomy, rotation, and resetting, and perform pre-surgery perfectly. Furthermore, there is no need to wait for the data to be imported into the surgical simulation operating system after completion of the preoperative CT examination, and the corresponding operation can be carried out, which is a research direction attracting much attention.¹⁸

Following the principles of Health Informatics, the successful adoption of complex surgical technologies relies not only on technical accuracy but also on the fit between the technology and the surgeon's task and the perceived ease of use. Our platform, as a form of contactless digital planning service, shifts preoperative planning from a purely mental exercise to an interactive, data-driven process. From a Digital Health perspective, our platform functions as a specialized Clinical Decision Support system. Similar to the implementation of business intelligence in healthcare organizations, our system synthesizes complex patient-specific DICOM data into a simplified metric-based recommendation for implant selection. Furthermore, ensuring the transparency and traceability of these digital plans is crucial; future iterations of the platform could explore blockchain-based knowledge sharing to secure surgical planning data across institutions.¹⁹

In our study, the operating time was significantly reduced when the operation simulation platform was used before surgery. Although there was no statistical difference, the knee joint function and mobility of patients in the intelligent assistant group after fracture were better than in the patients in the traditional surgery group. We believe that this is related to the use of the surgical simulation operating system, which allows physicians to obtain a clearer understanding of tibial plateau fractures. Through 3D image simulation, each bone block can be automatically divided and identified, and doctors can easily obtain information on the key bone blocks, such as the number, size, and location of collapsed bone blocks on the articular surface and the anatomical reduction markers on each bone block. By hiding, moving, and rotating bone blocks, doctors gain knowledge of the sequence of bone block reduction and the reduction effect. If the simulated fracture reduction effect is poor, the simulation can be repeated to identify and overcome the cause of poor reduction. Finally, the fracture block can be reduced and fixed in a planned way according to the results of the simulated operation. Our results show that the quality of fracture reduction can be improved, the reduction of a critical bone mass is not missed, and the operating time can be shortened by performing several simulated operations before the actual surgery. With the improved fracture reduction, the patients’ knee function and motion were better than those in the traditional surgery group.

One of the advantages of our intelligent surgical simulation platform is that it can automatically generate a 3D simulation image of the fracture by importing the patient's thin-slice CT file into the system, and each major bone block can be automatically identified, segmented, and stained so our system can be applied to all parts of the fracture, not just the tibial plateau fracture. There are few other reports on intelligent assistant systems in trauma orthopedics. Zheng et al. reported that an intelligent assistant system improved the reduction of acetabular fractures and shortened the operating time (155 vs. 182 minutes).²⁰ Blood loss was reduced (430 vs. 570 mL), but there was no significant difference in hip function or fracture reduction. Chen et al. reported similar results using an intelligent assistance system; the acetabular fracture operating time was shortened by 43 minutes, and the blood loss was reduced by 130 mL.²¹ Jeon et al. reported the application of an intelligent assistance system in Neer 3/4 partial fractures of the proximal humerus and found greater consistency in proximal humerus fracture classification, better fracture reduction, and improved postoperative function with the use of the intelligent assistance system.²² Jia et al. used a preoperative surgical simulation system in elderly patients with intertrochanteric fractures and found that compared with conventional planning, the intelligent assistance group had lower mortality (9.1% vs. 13.5%), fewer postoperative complications (6.1% vs. 10.8%), and a lower reoperation rate (0.76% vs. 0.97%).²³ Operation simulation aided the treatment of intertrochanteric fractures in the elderly. Souleiman et al. reported the clinical effect of using a 3D image system in tibial flat fracture surgery.²⁴ In their study, the operating time in the 3D auxiliary group was longer (127.9 vs. 116.1 minutes), while the postoperative KOOS score was significantly better (72.6 vs. 62.0 points). The intraoperative use of 3D image systems can improve the prognosis, but prolong the operating time, while we use 3D image simulation before surgery, which reduces the operating time. There are some similarities between the reported surgical simulation operating systems and our system, but our system has stronger bone block recognition ability, clearer images, more operational functions, and a wider range of applications. We plan to expand the use of this system to clinical fracture treatment to verify its effectiveness and continue to improve it.

Our study has several shortcomings. Although prospective 1:1 randomization was adopted in this experiment, the study was not double-blind, which might affect the results. Second, the most important is the small sample size (N = 28). This was a pilot study designed to evaluate the feasibility and preliminary efficacy of the digital simulation platform. As such, the study was not adequately powered to detect statistically significant differences in functional outcomes. The non-significant p-values for knee Rasmussen score and knee range of motion should be interpreted with caution. These findings do not demonstrate equivalence or absence of benefit; rather, they reflect a high probability of Type II error (false negative). Finally, our surgical simulation operating platform can only achieve fracture reduction, and cannot perform operations such as adding internal fixation, which is of limited help in the selection of internal fixation during surgery. Moreover, the system could not identify some very small bone blocks and connected them to the side of larger bone blocks, which caused problems with the preoperative planning and may also explain why two patients in the intelligent auxiliary group failed to achieve excellent fracture reduction. The promising trends observed in this pilot study provide a strong rationale for a definitive randomized controlled trial. We propose that a multicenter RCT is now warranted to confirm the functional benefits of digital surgical simulation in complex tibial plateau fractures.

Conclusion

Digital surgical simulation platforms are powerful tools for the treatment of complex tibial plateau fractures. They can enable accurate preoperative planning, help doctors better understand the fracture morphology to choose the best surgical method, simulate relevant surgical operations, and evaluate the surgical effect. Their use can significantly reduce operating times, facilitate fracture reduction, and improve knee function after a complex tibial plateau fracture.

Footnotes

ORCID iD

Erman Chen

Ethical approval

Author contributorship

Conceptualization, E.M.C. and L.J.J.; investigation, Y.J.Z. and H.X.Z.; resources, E.M.C.; writing—original draft preparation, E.M.C.; writing—review and editing, L.J.J. All authors have read and agreed to the published version of the manuscript.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

References

Pedrazzini

Garzia

Bertoni

, et al. High energy trauma with complex fracture of the right tibial plateau, compartment syndrome and infection in a professional freestyler motorcyclist. Acta bio-medica: Atenei Parmensis 2020; 91: e2020018.

Goulet

Bray

. Complex acetabular fractures. Clin Orthop Relat Res 1989; 240: 9–20.

Michel

Raschke

Katthagen

, et al. Double plating for Complex proximal humeral fractures. Clinical and Radiological Outcomes 2023; 12: 696.

Langerhuizen

DWG

Janssen

Mallee

, et al. What are the applications and limitations of artificial intelligence for fracture detection and classification in orthopaedic trauma imaging? A systematic review. Clinical Orthopaedics and Related Research 2019; 477: 2482–2491.

Kalmet

PHS

Sanduleanu

Primakov

, et al. Deep learning in fracture detection: a narrative review. Acta Orthop 2020; 91: 215–220.

Zhao

Ren

, et al. Evolution and current applications of robot-assisted fracture reduction. A Comprehensive Review 2020; 48: 203–224.

Hsu

Gross

Bhatia

, et al. Template-directed instrumentation in total knee arthroplasty: cost savings analysis. Orthopedics 2012; 35: e1596–e1600.

Jentzsch

Vlachopoulos

Fürnstahl

, et al. Tumor resection at the pelvis using three-dimensional planning and patient-specific instruments: a case series. World J Surg Oncol 2016; 14: 49.

Cartiaux

Banse

Paul

, et al. Computer-assisted planning and navigation improves cutting accuracy during simulated bone tumor surgery of the pelvis. Comput Aided Surg 2013; 18: 19–26.

10.

Park

. Computed tomography-based preoperative simulation system for pedicle screw fixation in spinal surgery. J Korean Med Sci 2020; 35: e125.

11.

Wang

Luo

, et al. One visualization simulation operation system for distal femoral fracture. Medicine (Baltimore) 2017; 96: e7770.

12.

Boudissa

Courvoisier

Chabanas

, et al. Computer assisted surgery in preoperative planning of acetabular fracture surgery: state of the art. Expert Rev Med Devices 2018; 15: 81–89.

13.

Nijsink

Arts

Verhamme

, et al. The value of digital 3D models in evaluating surgical outcomes using the uninjured contralateral acetabulum after acetabular fracture repair. Injury 2023; 54: 1169–1175.

14.

Oosterhoff

JHF

Doornberg

. Artificial intelligence in orthopaedics: false hope or not? A narrative review along the line of gartner's hype cycle. EFORT open Reviews 2020; 5: 593–603.

15.

Storelli

Bauer

Lattanza

, et al. The use of computer-aided design and 3-dimensional models in the treatment of forearm malunions in children. Tech Hand Up Extrem Surg 2015; 19: 23–26.

16.

Buschbaum

Fremd

Pohlemann

, et al. Computer-assisted fracture reduction: a new approach for repositioning femoral fractures and planning reduction paths. Int J Comput Assist Radiol Surg 2015; 10: 149–159.

17.

Jiang

Huang

. The efficacy of3DPrinting model in the intraarticular osteotomy in the treatment of malunion of tibial plateau fracture. Orthop Surg 2023; 15: 85–92.

18.

Stübig

Windhagen

Krettek

, et al. Computer-Assisted orthopedic and trauma surgery. Dtsch Arztebl Int 2020; 117: 793–800.

19.

Dbesan

Abdulmuhsin

Alkhwaldi

. Adopting knowledge-sharing-driven blockchain technology in healthcare: a developing country’s perspective. VINE J Inf Knowl Manag Syst 2025; 55: 683–709.

20.

Zheng

Chen

Yang

, et al. Application of computerized virtual preoperative planning procedures in comminuted posterior wall acetabular fractures surgery. J Orthop Surg Res 2022; 17: 51.

21.

Chen

Zheng

Fang

, et al. One-stop computerized virtual planning system for the surgical management of posterior wall acetabular fractures. J Orthop Surg Res 2022; 17: 39.

22.

Jeon

Jung

. Clinical validation of artificial intelligence-based preoperative virtual reduction for neer 3- or 4-part proximal humerus fractures. BMC Musculoskelet Disord 2024; 25: 669.

23.

Jia

Zhang

Qiang

, et al. Association of computer-assisted virtual preoperative planning with postoperative mortality and complications in older patients with intertrochanteric hip fracture. JAMA network Open 2020; 3: e205830.

24.

Souleiman

Henkelmann

Theopold

, et al.

Intraoperative 3D imaging in intraarticular tibial plateau fractures - does it help to improve the patients’ outcomes?

J Orthop Surg Res 2021; 16: 295.

Application of a digital surgical simulation platform in the treatment of Complex tibial plateau fractures

Abstract

Objective

Methods

Results

Conclusion

Keywords

Introduction

Materials and methods

Patient enrollment

Randomization

Preoperative planning

Description of the digital surgical simulation platform

Surgeon and implants

Surgical technique

Postoperative management

Follow-up and outcomes

Statistical analysis

Results

Baseline characteristics

Clinical outcomes

Discussion

Conclusion

Footnotes

ORCID iD

Ethical approval

Author contributorship

Funding

Declaration of conflicting interests

References