Digital twins in traumatology and orthopedics: review of joint imaging and cost-effectiveness

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INTRODUCTION

A digital twin (DT) is a virtual representation that reproduces the state and changes of a specific object in near real-time [1]. The concept of DTs was originally developed in industry and engineering, where it enabled process optimization and prediction of system behavior without risks to real-world objects. In recent years, DTs have been increasingly applied in medicine, including traumatology and orthopedics, opening new opportunities for personalized diagnosis, treatment planning, and outcome prediction [2]. The high prevalence of musculoskeletal disorders (e.g., osteoarthritis and post-traumatic conditions), along with associated disability and economic burden, drives the search for new therapeutic approaches. A joint DT, integrating medical imaging data (magnetic resonance imaging, computed tomography, etc.), biomechanical parameters, and artificial intelligence (AI) algorithms, represents such an approach, enabling the testing of various therapeutic strategies in a virtual environment without risk to the patient [2].

Digital modeling methods are particularly relevant in the context of preclinical evaluation of novel osteogenesis stimulation techniques. Experimental animal studies investigating orthobiological products (cell-based therapies, tissue matrices) require objective and reproducible quantitative assessment of bone regeneration. Traditional radiographic scales are often subjective. The application of approaches derived from the DT concept, such as 3D reconstruction based on micro–computed tomography (CT) data, precise morphometry of the bone callus, and assessment of its mineral density, enables not only to evaluate the osteogenic potential of the studied product but also to distinguish biological effects from biomechanical factors (e.g., fixation stability, interfragmentary gap size). Early studies have already demonstrated the potential of DTs in orthopedics. For example, knee joint prototypes based on quantitative magnetic resonance imaging (qMRI) have been developed to monitor degenerative changes in osteoarthritis and to support arthroplasty planning. Digital models are also being developed to optimize surgical strategies in complex fractures and arthroscopic procedures. Another important direction is the advancement of joint imaging. Novel techniques, such as 3D computed tomography combined with multiplanar CT arthrography, remarkably improve the diagnostic accuracy of joint condition compared with traditional approaches [3]. Additionally, the implementation of DTs and related digital technologies in healthcare has important economic implications, including resource optimization and cost reduction through decreased unnecessary procedures and complications. Studies evaluating the effectiveness and cost-effectiveness of such technologies, particularly navigation and robotic systems in orthopedics, are already underway [4]. However, challenges remain regarding model validity, data integration, and legal and ethical constraints related to the use of digital patient representations.

This review evaluates the application of DTs in clinical traumatology and orthopedics, with a focus on joint imaging and cost-effectiveness across the following domains: preoperative planning in deformities and trauma; modeling of osteoarthritis progression; decision-making in femoroacetabular impingement (FAI); and risk assessment of revision and other complex surgical interventions.

SEARCH METHODOLOGY

The review was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A total of 10 studies meeting the inclusion criteria were included in the systematic review: 7 original experimental or clinical studies and 3 systematic reviews (Fig. 1).

 

Fig. 1. PRISMA flow diagram: scientific data selection process for the review.

 

QUALITY ASSESSMENT AND RISK OF BIAS

Quality assessment and risk of bias were evaluated within a health technology assessment (HTA) framework (GOST R 56044-2014¹), taking into account the design of the included studies (Supplement 1). The primary tools used were ROBINS-I (Risk Of Bias In Non-randomized Studies) for observational and modeling studies and AMSTAR-2 (Assessment of Multiple Systematic Reviews 2) for systematic reviews. For selected diagnostic and prognostic publications, a narrative comparison of key reporting elements was performed in accordance with the STARD (Standards for Reporting of Diagnostic Accuracy Studies) and TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) / TRIPOD-AI (TRIPOD incorporating artificial intelligence) guidelines. Formal assessment using QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2) and PROBAST (Prediction model Risk Of Bias ASsessment Tool), including score-based evaluation, was not performed due to heterogeneity of study designs and incomplete reporting. Final categories of risk of bias (low, moderate, high, or unclear) were assigned by consensus among the authors (summary results are presented in Supplement 2 [4–12]).

Notably, some sources were considered particularly valuable. For example, the corporate publication by Prokhorov et al. [13], although not presented as a systematic review, provides an overview of the current state of the issue and includes references to systematic reviews and original studies. Therefore, such sources were not excluded but were retained as background data in the supplementary materials.

The geographic distribution of studies includes both international (USA, Europe, Asia) and Russian publications, reflecting global interest in the issue. However, many Russian publications represent expert opinions lacking formal methodology and an evidence base.

PURPOSE OF THE INCLUDED STUDIES

Digital Twins of Joints and Surgical Interventions

Several studies have focused on the development and application of DTs of joints. For example, Hoyer et al. [5, 7] presented a knee joint prototype based on quantitative magnetic resonance imaging (MRI; cartilage T2 mapping) combined with AI algorithms. This digital twin is designed to predict osteoarthritis progression and to support personalized planning of total knee arthroplasty.

Diniz et al. [14] highlighted that digital twins are increasingly applied in orthopedics, ranging from exercise monitoring and individualized rehabilitation to modeling joint mechanics and predicting postoperative outcomes. Dean et al. [15] discussed how digital patient models can improve orthopedic assessment, from preoperative 3D planning of osteotomies to outcome prediction using augmented and virtual reality technologies.

Aubert et al. [9] developed a personalized digital twin to optimize the treatment of tibial plateau fractures, incorporating biomechanical modeling of injury and assessment of postoperative complication risks. Key parameters in such models include fragment displacement vectors, angular deformity, and the volume and density of the forming bone callus. These metrics, obtained from CT, form the basis for predictive models of bone healing and may serve as sensitive biomarkers for evaluating the effectiveness of orthobiological products in experimental settings.

Hein et al. [8] developed a prototype for spinal surgery planning that integrates a 3D model of spinal anatomy, surgical instruments, and simulation of surgeon actions. Although published as a preprint (proof of concept), this study demonstrates the technical feasibility of integrating imaging data and procedural simulation within a unified digital surgical environment.

Bjelland et al. [10] conducted a systematic review of approaches that could underpin DTs for arthroscopic knee surgery. The authors emphasized that recent studies have focused on interactive modeling of soft tissues (ligaments and menisci) and haptic feedback for training simulators. Although direct implementations of DTs in arthroscopy remain limited, the review highlights their substantial potential in minimally invasive joint surgery, provided that real-time modeling capabilities continue to improve. Prototypes and case studies of personalized DTs have been reported for knee joints (qMRI/T2 mapping combined with AI for osteoarthritis prediction and total knee arthroplasty planning), as well as for tibial plateau fractures and spinal surgery (proof of concept). The potential of DTs in minimally invasive arthroscopy is supported by systematic evidence; however, fully functional real-time DTs remain scarce.

Imaging and Segmentation Techniques for Joint Digital Twins

A separate body of research focuses on medical imaging tools required for the development of high-resolution digital joint models. For example, in a review study, Sun et al. [16], highlighted the advantages of integrating DTs into clinical practice and identified key technological challenges, including the acquisition of reliable imaging data reflecting tissue status.

Modern imaging techniques enable high-precision 3D visualization of bone and cartilage structures, and the diagnostic performance of noninvasive modalities has approached the gold standard of diagnostic arthroscopy. Chuang et al. [3] demonstrated that the combined use of 3D CT and multiplanar CT arthrography of the hip joint provides high diagnostic accuracy for FAI and associated lesions (labral tears and chondral defects) compared with arthroscopy. This combined imaging approach has been proposed as an effective alternative to MRI and MR arthrography in preoperative planning for hip impingement. Such multimodal techniques enable the generation of detailed 3D joint models and may serve as a valuable alternative when conventional MRI or MR arthrography is insufficient [17].

Segmentation of MRI data is widely used for modeling articular cartilage. Several studies [18–20] have demonstrated that deep learning-based algorithms (e.g., U-Net architectures) can automatically segment cartilage of the knee and wrist joints with accuracy comparable to that of expert radiologists, enabling rapid reconstruction of 3D joint models for subsequent DT development. An important advancement is the automated generation of cartilage property maps (e.g., T2 maps for assessing cartilage degeneration), as reported by Thomas et al. [20].

Thus, modern joint imaging technologies, from multidetector CT to advanced MRI combined with AI, provide the foundation for DT development by supplying reliable data on tissue morphology and condition. The basis consists of 3D CT (bone) and MRI/qMRI (cartilage and soft tissues); automated segmentation (e.g., U-Net–based architectures) enables accelerated reconstruction of 3D models. CT arthrography is considered in selected cases (e.g., FAI, wrist joint condition, or pronounced metal artifacts), provided that appropriate expertise and radiation dose control are ensured.

Data Analysis and Modeling of Disease Progression

Several studies focus on the use of big data and computational models to predict outcomes of orthopedic diseases using DTs. Quantitative MRI biomarkers and machine learning (ML) are used to assess the risk of osteoarthritis progression and the timing of total joint replacement; some studies have also explored semantic knowledge enrichment using natural language processing (NLP) to enhance DTs. For example, Hoyer et al. [7] applied quantitative MRI biomarkers and machine learning to analyze the progression of knee osteoarthritis and the time to joint replacement. In their study, published as a preprint, it was shown that a combination of multiple MRI-derived cartilage and meniscal parameters, analyzed within a digital joint model, may help identify biomarkers associated with disease deterioration and impending surgery. Amirian et al. [21] approached the problem from a different perspective: through analysis of scientific texts. Using neural network-based word embedding models, they extracted specialized terminology from thousands of publications on knee osteoarthritis, enabling the development of a semantic knowledge base for further enrichment of digital disease models. Although this work was presented as conference proceedings and has not undergone full peer review, such approaches illustrate that DTs can be enriched not only with imaging data but also through integration of literature-derived information on risk factors, treatment options, and outcomes. In the future, such data-rich DTs may serve as decision-support tools, allowing clinicians to simulate different treatment scenarios for individual patients using their digital counterparts, incorporating statistically validated disease patterns.

Sun et al. [2] note that traditional biomechanical methods for studying the musculoskeletal system often fail to account for individual and dynamic aspects, whereas the implementation of DTs is intended to overcome these limitations and advance orthopedics toward personalized medicine.

Cost-Effectiveness and Value of Implementing Digital Technologies

The final, yet critically important, aspect addressed in this review concerns the economic and organizational implications of implementing DTs in clinical practice. Systematic reviews demonstrate variable results: potential cost-effectiveness may be achieved at high procedural volumes and with reductions in revisions and complications; however, these findings require independent validation. Risks of publication and financial bias have also been identified. The included studies demonstrate that digital technologies in orthopedics may reduce costs when used appropriately, although the evidence remains inconsistent. In a systematic review by Li et al. [4], the cost-effectiveness of computer-assisted orthopedic surgery (including navigation and robotic systems) was analyzed. In some cases, these technologies were found to be economically justified. For example, robotic total hip arthroplasty was shown to be a dominant strategy (better outcomes at lower cost) over a 5-year horizon, and in model-based analyses over a 10-year period demonstrated an incremental cost-effectiveness ratio (ICER) of £1910 per quality-adjusted life year (QALY), well below the commonly accepted threshold of £20,000/QALY [22]. High cost-effectiveness was also observed in scenarios involving robotic systems at high surgical volumes (more than 100 procedures per year) and in younger patients undergoing unicompartmental knee arthroplasty, primarily due to a reduction in revision rates [23, 24]. In contrast, for other interventions (e.g., robot-assisted spinal fusion), the impact on outcomes was minimal despite substantially higher costs, raising concerns regarding the economic value of such technologies [23]. Moreover, a systematic bias was identified: 91% of studies comparing robotic and conventional techniques involved authors with financial conflicts of interest, and these studies were more likely to report favorable outcomes for robotic systems [24]. The presence of such bias raises concerns about the objectivity of the available evidence and underscores the need for independent comparative studies. These findings highlight the importance of cautious interpretation of the existing data. Russian researchers also emphasize that healthcare digitalization has substantial economic implications. Digital innovations may improve process efficiency and reduce duplication of diagnostic procedures, ultimately lowering costs and accelerating patient care. However, as noted by Zuenkova [25], despite their potential to optimize healthcare resources, DTs require substantial investment and the overcoming of administrative and regulatory barriers.

Overall, the economic impact of DTs in orthopedics will depend on multiple factors, including the cost of model development and maintenance, their effect on reducing complications and revision surgeries, and the scale of implementation. Current evidence suggests cautious optimism, demonstrating several examples of clinical and economic benefits of digital technologies (e.g., improved diagnostic accuracy [3] and reduced revision rates and associated costs [4]). However, further prospective studies are required to draw robust conclusions, particularly in the context of Russian healthcare systems. The characteristics and key findings of all included studies are outlined in Supplement 2.

Prototypes of Digital Twins and Their Application Potential

The analysis showed that the concept of DTs is being actively implemented in orthopedics at multiple levels: from modeling individual structures (cartilage, ligaments) to comprehensive simulation of surgical procedures. Even pilot projects demonstrate the substantial practical value of DTs. For example, the knee joint DT developed by Hoyer et al. [7] integrates MRI data with AI-based analysis to predict osteoarthritis progression. This suggests that, in the future, clinicians may be able to obtain individualized prognostic assessments and, based on virtual simulations, select optimal management strategies, ranging from intensified conservative therapy to timely preparation for joint replacement. Digital surgical simulators described by Aubert et al. [9] and Hein et al. [8] open new opportunities for surgical training and preoperative planning of complex interventions. By virtually simulating procedures on a patient-specific DT, surgeons can identify the optimal surgical approach, determine appropriate fixation strategies, and anticipate potential complications. In the future, as such models continue to improve, we may move toward the concept of surgery without surprises, in which surgically complex cases are first practiced on a DT and only then performed on the patient.

Modern Imaging as the Foundation of Digital Twins

High-quality imaging is essential for the development of a fully functional DT. The results of this review confirm that diagnostic approaches to joint injuries have advanced substantially over the past decade. In particular, the use of 3D CT and novel imaging techniques such as arthrography has enabled diagnostic accuracy in detecting intra-articular hip lesions to approach that of arthroscopy [3]. In combination with automated cartilage segmentation on MRI, this allows for the construction of accurate geometric joint models with visualization of pathological changes (e.g., cartilage defects and ligament tears). Such models form the basis for biomechanical analysis: with precise information on joint geometry and tissue properties, it becomes possible to estimate loading conditions, wear patterns, and related parameters. Thus, improvements in imaging technologies directly expand the capabilities of DTs.

In the near future, further integration of AI into diagnostics is expected. Algorithms will not only segment anatomical structures but also classify their condition (e.g., healthy versus damaged cartilage, presence or absence of osteonecrosis, stage of osteoarthritis, type of meniscal tear), with these parameters automatically incorporated into DTs. The choice between MR and CT arthrography depends on the clinical question, availability, contraindications to MRI, and radiation exposure. MR arthrography is generally preferred for soft-tissue assessment, whereas CT arthrography is particularly useful for evaluating subtle osteochondral defects, FAI, wrist joint condition, and cases with pronounced metal artifacts.

In clinical traumatology and orthopedics, several applied scenarios for DT implementation can be identified:

1. FAI and post-traumatic hip deformities: 3D CT withmultiplanar CT arthrography for assessment of jointgeometry and chondrolabral lesions, and planning of bone reconstruction;
2. knee osteoarthritis: qMRI (T2 mapping) and automated segmentation of cartilage and menisci for risk stratification of disease progression andselection of timing for arthroplasty or joint-preservingprocedures;
3. intra-articular fractures (e.g., tibial plateau): CT combined with 3D modeling for optimization of osteosynthesis configuration and assessment of mechanical stability;
4. patellofemoral instability or pain syndrome: multibody modeling of patellar tracking to guide the choice between soft-tissue and bony correction;
5. shoulder instability: CT or MR arthrography with 3D modeling of bone defects and the glenoid labrum to determine indications for Latarjet procedure;
6. spinal deformities: patient-specific models for personalized correction and fixation planning.

In these scenarios, DTs improve the rationale for preoperative decision-making and reduce the risk of overcorrection, undercorrection, and revision procedures. From a clinical perspective, different data sources contribute distinct value to DT development. Imaging modalities (CT/MRI) provide accurate bone geometry and delineation of cartilage and labral structures (hip, shoulder, wrist), thereby improving 3D planning of marginal resections and refixation procedures. Quantitative MRI (T2 mapping, and T1ρ where available) provides biomarkers of early hyaline cartilage degeneration for predictive modeling. Automated segmentation reduces preparation time and interobserver variability (with mandatory visual validation in complex regions). Functional data on gait and loading conditions enhance the external validity of biomechanical predictions (e.g., contact pressures in the patellofemoral joint).

Model Accuracy and the Challenge of Validation

Accuracy and validity of digital twins remain one of the key issues. Despite impressive examples (e.g., modeling of patellar tracking with high agreement with experimental data [11]), the difficulty of realistically reproducing the biomechanics of living tissues is a common limitation. Soft tissues and articular cartilage are mobile, deformable structures. As noted by researchers, current simulations either require substantial computational resources to model deformations in real time or rely on simplified representations at the expense of accuracy. A potential solution is the transition to hybrid modeling approaches, for example, the use of multibody dynamics instead of computationally intensive finite element methods to accelerate calculations, as demonstrated by Michaud et al. [11], where an analytical contact algorithm was applied instead of full deformation modeling.

Validation is essential: DT outputs must be compared with real-world measurements (e.g., joint loading in the model versus gait or pressure sensor data). Without such calibration, a DT risks remaining a visually compelling model without clinical utility. Encouragingly, many studies already incorporate validation steps. For instance, in the aforementioned patellar model, simulation results were compared with experiments using a 3D-printed test setup [11].

To reduce heterogeneity and improve the robustness of conclusions in orthopedic applications, standardization of reporting and study design is required. For imaging studies, adherence to QUADAS-2 and STARD is recommended (clear reference standard, patient spectrum, flow and timing). For predictive models, TRIPOD-AI and PROBAST should be applied (separation of development and validation datasets, external validation, calibration, and handling of missing data). For economic evaluations, CHEERS (Consolidated Health Economic Evaluation Reporting Standards) and HTA (Health Technology Assessment) frameworks should be followed (time horizon, assumptions regarding revisions and complications, sensitivity analyses, and budget impact). Harmonization of imaging and segmentation protocols (e.g., slice thickness, reconstruction parameters, and quality control procedures) should be provided as supplementary material, as this improves reproducibility and comparability of results.

Organizational and Ethical Considerations

Despite rapid technological progress, equally substantial barriers lie in the domains of healthcare organization and ethics. First, the development and maintenance of patient-specific digital models will require substantial resources, ranging from advanced information technology infrastructure to dedicated staff training. It is also necessary to demonstrate the clinical value of these technologies to practitioners and integrate them into existing clinical workflows. At present, many institutions lack even standardized systems for storing 3D patient data, let alone dynamic DTs. Second, regulatory and standardization issues arise: responsibility for decisions made on the basis of DTs remains unclear, as does the protection of personal data, given that DTs effectively contain comprehensive patient information and may potentially be used beyond their intended purpose.

From a HTA perspective (GOST R 56044-2014),¹ the implementation of DTs should be considered as the assessment of a health technology requiring transparent reporting of study design, comparator selection, and potential sources of bias. Zuenkova [25] emphasizes regulatory constraints as one of the key barriers to DTs implementation. In the European Union and the United States, discussions are already underway regarding the certification of AI-based medical systems, and DTs are likely to be regulated as medical devices. Third, ethical considerations remain critical. The possibility of cloning a patient in a virtual environment raises questions regarding informed consent, privacy, and appropriate scope of use: for example, whether it is acceptable to use a DT to test interventions that would not be permissible in vivo, or to train algorithms that are subsequently used for commercial purposes. These questions currently lack clear answers [1].

Practical implementation framework for DTs in traumatology and orthopedics:

1. selection of 1–2 narrowly defined clinical scenarios (e.g., FAI and tibial plateau fractures);
2. establishment of standardized imaging protocols and segmentation quality control;
3. development of 3D models and initial DTs (multibody/finite element models as appropriate);
4. pilot evaluation comparing DT–based planning with actual intraoperative decisions and outcomes;
5. prospective collection of predefined clinical, diagnostic, and resource-related metrics (including PROMs; see below);
6. iterative model refinement through a feedback loop;
7. economic evaluation (sensitivity to procedural volume, model development costs, and reductions in revisions and operative time);
8. scaling to additional clinical scenarios following demonstration of clinical and organizational benefit.

Future Directions

The findings of this review suggest that rapid development of DT technologies in orthopedics can be expected in the coming years.

For clinical relevance and economic interpretation in orthopedic practice, it is advisable to capture the following metrics:

• perioperative metrics (operative time, conversion of surgical approach, blood loss);
• mid-term outcomes (revision procedures, reoperations, complications);
• patient-reported outcome measures (PROMs), including instruments for subjective assessment of knee and hip function and patient well-being, suchas the International Knee Documentation Committee(IKDC), Knee Injury and Osteoarthritis Outcome Score (KOOS), Hip Disability and Osteoarthritis Outcome Score (HOOS), Oxford Knee Score (OKS), Harris Hip Score (HHS), and Kujala score, assessed at 3, 6, and 12 months;
• diagnostic performance metrics (agreement with the reference standard, arthroscopy, for imaging-based scenarios);
• resource-related metrics (cost of model development, CT/MRI utilization, and reduction in operative time).

Such a set of metrics facilitates comparability across studies and increases the robustness of conclusions.

Promising directions for further development include:

• integration of multiscale models, from molecular-level processes (e.g., cartilage regeneration modeling) to organ-level and system-level representations (including comorbidities in outcome prediction);
• application of DTs in rehabilitation, for example, development of patient-specific DT following arthroplasty to optimize recovery protocols;
• population-level applications of DTs, including the concept of a digital twin cohort, in which aggregated patient data are used to construct generalized models capable of forecasting healthcare demand (e.g., orthopedic service burden over a 10-year horizon) and estimating economic impacts of new treatment strategies.

In the context of cost-effectiveness, as demonstrated by Li et al. [4], healthcare systems are increasingly motivated to adopt solutions that reduce costs without compromising quality of care. The prioritization of digitalization as a key direction in healthcare development is evident, and DTs occupy a strong position in this process: their ability to prevent complications, for example, through more accurate implant positioning [26], and to avoid unnecessary interventions translates directly into cost savings. Improvement in patient’s quality of life is equally important and represents the ultimate goal of medical innovation.

It should also be noted that the majority of current studies on DTs of bone tissue focus primarily on joint biomechanics or purely mechanical aspects of osteosynthesis. There is a notable lack of studies integrating orthobiological data (e.g., cell-based therapies) into digital models, creating a gap between advanced imaging technologies and tissue engineering. Experiments such as our study on a rabbit fracture model will make it possible in the future to enrich DTs with data not only on geometric parameters but also on the biological potential of regeneration, representing the next step toward personalized prediction of consolidation timelines.

FUTURE DIRECTIONS AND LIMITATIONS OF DIGITAL TWIN IMPLEMENTATION

DTs represent a new paradigm in traumatology and orthopedics, integrating advances in medical imaging, biomechanical modeling, and AI to improve patient care. This systematic review (2020–2025) demonstrated that:

1. early prototypes of joint DTs are already being successfully applied for osteoarthritis prediction and surgical planning, demonstrating clinical potential;
2. modern imaging techniques (3D CT and MRI with AI-based segmentation) provide a high-precision foundation for constructing digital joint models;
3. the application of DTs and related technologies mayimprove treatment efficiency and potentially reduce healthcare costs, although economic conclusions require further independent validation;
4. key challenges include ensuring biomechanical accuracy of models, integration of DTs into clinical workflows, and addressing issues of standardization, responsibility, and data protection.

The practical value of DTs is most evident in specific orthopedic scenarios, including preoperative planning for FAI and post-traumatic joint deformities; optimization of osteosynthesis configuration in intra-articular fractures (e.g., tibial plateau fractures); personalized prediction of knee osteoarthritis progression and timing of arthroplasty; and decision-making in patellofemoral instability.

The data foundation is formed by multimodal protocols: high-resolution 3D CT for bone geometry; MRI/qMRI (T2 mapping) for assessment of cartilage and soft tissues; and CT arthrography when indicated (e.g., FAI, wrist joint condition, or pronounced metal artifacts), provided appropriate expertise and dose control are ensured. Expected clinical effects include reduction in unplanned intraoperative changes, residual impingement or instability, revision rates, and operative time; relevant outcome measures include PROMs (IKDC, KOOS/HOOS, Kujala/OKS, HHS), complications, and reoperations.

Overall, a low risk of bias was not confirmed for any of the included studies; moderate to high risk predominated, reflecting observational and modeling study designs, small sample sizes, and limited external validation. Detailed assessments and justification are provided in the Supplement (Section S2). These limitations necessitate cautious interpretation of the findings and highlight the need for multicenter studies with external validation of reproducibility. It should also be noted that formal scoring using QUADAS-2 and PROBAST was not performed for diagnostic and prognostic studies; instead, a qualitative assessment of key domains and reporting elements was conducted in accordance with STARD and TRIPOD principles. This approach further limits the level of confidence in the overall conclusions.

Key directions for future development include:

1. standardization of study design and reporting: QUADAS-2/STARD for diagnostic accuracy studies; PROBAST/TRIPOD-AI for predictive models; CHEERS and HTA-based approaches for economic evaluations;
2. external validation of patient-specific models and quality assessment of automated segmentation;
3. clinical and economic studies in real-world practice, including sensitivity analyses and budget impact assessment;
4. stepwise implementation of DTs in traumatology and orthopedic departments according to the algorithm described in the Discussion section. Under conditions of sufficient procedural volume and confirmed reduction in revisions and complications, the cost-effectiveness of DTs appears likely; however, this requires independent validation.

The most promising direction of development appears to be the convergence of two fields: the development of novel orthobiological approaches to stimulate tissue regeneration and the advancement of digital methods for evaluating their effectiveness. Only an interdisciplinary approach, integrating expertise from biology, surgery, and medical imaging, will enable the creation of validated digital models capable of predicting the effects of specific osteoplastic materials under defined biomechanical conditions. The results of planned experimental studies comparing gingival and placental tissue-derived products are expected to contribute to the development of such an evidence base.

Future development of DTs in orthopedics will likely focus on three main areas: increased biomechanical realism (e.g., modeling of muscle forces and physiological responses to interventions), expansion of application domains (rehabilitation, sports medicine, injury prevention), and accumulation of evidence supporting their effectiveness [14, 27]. Fostering interdisciplinary collaboration among clinicians, engineers, IT specialists, and health economists is equally important for successful implementation of digital twins in practice [25, 28, 29]. It can be expected that within the next 5–10 years, DTs will become an integral part of planning complex orthopedic procedures and surgical training, and, in the longer term, of personalized monitoring of joint health in high-risk patients. The implementation of these technologies is expected to be gradual, driven by the accumulation of successful use cases and the development of regulatory frameworks. Nevertheless, it is already evident that DTs in traumatology and orthopedics are not a transient trend but an objective direction of development capable of improving both the quality and sustainability of healthcare. Their further study and implementation align with the interests of clinicians, patients, and healthcare systems as a whole.

CONCLUSION

This review was conducted to assess the current state of implementation of DTs in traumatology and orthopedics within the Russian healthcare system; to evaluate the economic feasibility of implementing joint DTs based on advanced diagnostic methods, including MR/CT arthrography and automated cartilage segmentation; and to summarize the current level of development of this issue in international publications and global practice.

The analysis confirms that DTs are a promising tool for personalized medicine in traumatology and orthopedics. Modern imaging techniques (3D CT and MRI with T2 mapping), in combination with AI algorithms, provide a reliable foundation for the creation of high-precision virtual joint models. DT prototypes already demonstrate potential in preoperative planning and prediction of osteoarthritis progression, which may contribute to reducing complications and revision procedures. Further development of this field requires close interdisciplinary collaboration among orthopedic surgeons, radiologists, biomechanical engineers, and health economists. Only such a comprehensive approach will enable standardization of technologies, robust clinical and economic validation, and integration of DTs into real-world clinical workflows, thereby opening new opportunities for improving patient outcomes.

ADDITIONAL INFORMATION

Suppelment 1. Background and clinical sources that enhance context but are not part of the systematic PRISMA analysis sample. Detailed assessment of the risk of systematic error.

doi: 10.17816/clinpract688314-4404019

Suppelment 2. Digital twins in traumatology and orthopedics: risk of bias assessment of included sources.

doi: 10.17816/clinpract688314-4406525

Author contributions: A.V. Pepelyaev, A.P. Prizov, A.A. Almazov, F.N. Kadyrov, study concept and design; A.V. Pepelyaev, V.D. Gorpinich, A.A. Muraev, A.A. Almazov, F.N. Kadyrov, data collection, analysis and interpretation; A.V. Pepelyaev, A.V. Petryaykin, N.V. Zagorodniy, F.N. Kadyrov, A.A. Almazov, manuscript editing and preparation. Thereby, all authors provided approval of the version to be published and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Funding sources: The study had no sponsorship.

Disclosure of interests: The authors declare no conflict of interests.

Statement of originality: The authors declare that the submitted manuscript is an original scientific work. All materials and data from other authors used in the work have appropriate citations in the text and are included in the reference list. The work has not been previously published and is not under consideration by other publications.

Data availability statement: The journal’s data availability policy does not apply to this work, as the article is a systematic literature review conducted according to the PRISMA methodology and does not contain previously unpublished primary data. All analyzed sources are publicly available and are listed in the reference list.

Generative AI: Generative AI technologies were not used for this article creation.

 

¹ National Standard of the Russian Federation. GOST R 56044-2014. Group R24. Evaluation of Medical Technologies. General Regulations. Available at: https://docs.cntd.ru/document/1200111499. Accessed on: February 15, 2026.

About the authors

Aleksandr V. Pepelyaev

Peoples’ Friendship University of Russia; Higher School of Economics

Author for correspondence.
Email: a.pepelyaev@mail.ru
ORCID iD: 0009-0005-5143-6175
SPIN-code: 7319-5591
Russian Federation, Moscow; Moscow

Aleksey P. Prizov

Peoples’ Friendship University of Russia

Email: aprizov@yandex.ru
ORCID iD: 0000-0003-3092-9753
SPIN-code: 6979-6480

MD, PhD, Professor

Russian Federation, Moscow

Alexey V. Petryaikin

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Email: Alexeypetraikin@gmail.com
ORCID iD: 0000-0003-1694-4682
SPIN-code: 6193-1656

MD, PhD, Assistant Professor

Russian Federation, Moscow

Alexandr A. Muraev

Peoples’ Friendship University of Russia

Email: muraev_aa@pfur.ru
ORCID iD: 0000-0003-3982-5512
SPIN-code: 1431-5936

MD, PhD, Professor

Russian Federation, Moscow

Nikolay V. Zagorodniy

Peoples’ Friendship University of Russia

Email: zagorodniy51@mail.ru
ORCID iD: 0000-0002-6736-9772
SPIN-code: 6889-8166

MD, PhD, Professor, academician of the Russian Academy of Sciences

Russian Federation, Moscow

Andrew A. Almazov

Higher School of Economics; N.A. Semashko National Research Institute of Public Health

Email: andrew@aalmazov.ru
ORCID iD: 0000-0002-8547-5667
SPIN-code: 2375-7962

Master of Public Health

Russian Federation, Moscow; Moscow

Farit N. Kadyrov

Higher School of Economics; N.A. Semashko National Research Institute of Public Health

Email: kadyrov@mednet.ru
ORCID iD: 0000-0003-4327-4418
SPIN-code: 7200-2000

Dr. Sci. (Economics), Professor

Russian Federation, Moscow; Moscow

Valerii D. Gorpinich

Saint Petersburg Scientific Research Institute of Ear, Throat, Nose and Speech

Email: gvalera251@bk.ru
ORCID iD: 0000-0002-7561-9188
SPIN-code: 5994-4177
Russian Federation, Saint Petersburg

References

Supplementary files