A Theoretical Bayesian Decision Tree Model for Indicating Robotic-Assisted versus Conventional Total Knee Arthroplasty

A Theoretical Bayesian Decision Tree Model for Indicating Robotic-Assisted versus Conventional Total Knee Arthroplasty | 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 A Theoretical Bayesian Decision Tree Model for Indicating Robotic-Assisted versus Conventional Total Knee Arthroplasty Francisco Endara Urresta, Carlos Peñaherrera-Carrillo, Alejandro Barros Castro, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8823642/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 31 Mar, 2026 Read the published version in Journal of Robotic Surgery → Version 1 posted 10 You are reading this latest preprint version Abstract Background Robotic-assisted total knee arthroplasty (RA-TKA) has demonstrated superior alignment accuracy and reduced variability compared to conventional techniques. However, the clinical indications for RA-TKA remain poorly standardized, often driven by surgeon preference or institutional availability rather than patient-specific complexity. There is a need for an objective model to guide case selection and promote rational use of robotic systems. Methods We developed a theoretical Bayesian decision tree model based on literature-derived conditional probabilities. The model integrates clinical variables (age, body mass index [BMI], coronal alignment, deformity severity, ASA classification) and system-level factors (robotic access) to estimate the posterior probability of benefit from RA-TKA. Simulated clinical scenarios and a Monte Carlo simulation with 10,000 virtual patients were used to evaluate model behavior and sensitivity. Results Coronal deformity ≥ 10° and BMI > 35 were the most influential variables, while robotic access acted as a binary gatekeeper. In simulated scenarios, posterior RA-TKA recommendation probabilities ranged from 14.6% to 89.2%, depending on complexity and access. The Monte Carlo simulation yielded a mean recommendation probability of 53.7% (SD 21.2%), with strong discriminatory performance. Sensitivity analysis confirmed the robustness of the model across input variations. Conclusions This Bayesian model provides a transparent, interpretable framework for RA-TKA indication. It supports evidence-based, individualized decision-making and offers a platform for standardizing the use of robotic technology. Future validation with institutional and multicenter datasets may allow for integration into clinical workflows and development of guideline-driven algorithms for robotic arthroplasty. Robotic-assisted total knee arthroplasty Clinical decision-making Surgical indication algorithm Predictive modeling Bayesian decision tree model Figures Figure 1 Figure 2 Figure 3 Introduction Total knee arthroplasty (TKA) has undergone substantial evolution over the past two decades, both in implant design and surgical technique. Among the most significant innovations is the incorporation of robotic assistance, which has emerged as a promising adjunct to improve surgical precision, restore mechanical alignment, and minimize variability in component positioning. Robotic-assisted TKA (RA-TKA) enables preoperative 3D planning and intraoperative adjustments based on soft-tissue behavior, offering a level of customization previously unattainable with conventional instruments. Clinical studies have reported improved radiographic outcomes, reduced outlier rates in coronal and sagittal alignment, and more consistent joint line restoration in patients undergoing RA-TKA. Although long-term survivorship and functional superiority remain subjects of ongoing investigation, early data suggest that robotic assistance may be especially beneficial in patients with complex deformities or biomechanical alterations. 1–3 Despite these potential advantages, the clinical indications for RA-TKA remain poorly defined. In current practice, the choice to utilize robotic technology is frequently influenced by surgeon preference, institutional policy, or logistical availability, rather than by standardized clinical criteria. As a result, patients who may derive the greatest benefit from the technology—such as those with severe coronal malalignment, excessive body mass index (BMI), or altered joint kinematics—may not consistently receive it. Conversely, RA-TKA may be employed in patients for whom conventional techniques would yield comparable outcomes, thereby contributing to unnecessary healthcare expenditure. This variability in clinical decision-making is further exacerbated by disparities in infrastructure and resource allocation, particularly in low- and middle-income health systems where robotic platforms may not be universally available. 1–3 There is therefore a pressing need for a reproducible, transparent, and clinically grounded algorithm to guide the appropriate indication of RA-TKA. An ideal decision model should integrate patient-specific factors—including age, BMI, degree of preoperative deformity, and limb alignment—as well as system-level variables such as hospital access to robotic systems and surgical expertise. Such a model would enable rational allocation of advanced technology based on anticipated clinical benefit, rather than arbitrary or economically driven considerations. 3–5 Bayesian decision trees represent a powerful and underutilized approach to clinical decision-making in orthopedic surgery. These models combine probabilistic reasoning with hierarchical classification, allowing clinicians to estimate the likelihood of benefit from a given intervention by sequentially integrating input variables. Unlike traditional regression models, Bayesian trees can accommodate conditional dependencies among predictors, handle uncertainty explicitly, and update themselves as new evidence emerges. Their intuitive structure and visual clarity make them especially well-suited for clinical environments, where decision support tools must be both interpretable and actionable. 3–5 In this context, we propose the development of a theoretical Bayesian decision tree to guide the indication of robotic-assisted versus conventional TKA. Rather than relying on retrospective institutional data, the model is constructed from a synthesis of published clinical evidence, incorporating key predictors identified in the literature. Through this approach, we aim to simulate decision pathways that reflect real-world variability and to visualize how specific clinical and institutional factors modulate the probability of benefit from robotic assistance. We hypothesize that a Bayesian decision model, based on literature-derived clinical and logistical variables, can provide a quantifiable and objective framework to support the selective indication of RA-TKA, thereby improving clinical consistency and rationalizing the use of robotic platforms. This theoretical foundation may inform future prospective validation studies and assist in the development of evidence-based guidelines for robotic technology in arthroplasty practice. Methods Conceptual Framework Bayesian decision trees are probabilistic graphical models designed to support decision-making under conditions of uncertainty. In the clinical setting, they offer a structured methodology to combine prior knowledge—often derived from clinical studies or expert consensus—with individual patient data to estimate the likelihood of a particular outcome or the appropriateness of a given intervention. Unlike traditional classification or regression trees, which segment data through deterministic splits at predefined thresholds, Bayesian trees assign conditional probabilities to each decision node. This structure allows the model to explicitly represent uncertainty and to update its output dynamically as new evidence becomes available. One of the key advantages of Bayesian decision trees over linear regression models is their ability to incorporate non-linear and conditional interactions between variables. For example, the effect of obesity on the technical complexity of total knee arthroplasty (TKA) may vary substantially depending on the degree of preoperative deformity or coronal alignment. Bayesian models accommodate such interdependencies naturally, without assuming independence or linearity. Moreover, unlike conventional algorithms that often act as black boxes, Bayesian trees produce a transparent and interpretable structure. Each node and branch can be traced and clinically justified, making them suitable as decision-support tools in surgical environments, particularly where interpretability is essential for adoption by orthopedic surgeons. Bayesian decision trees are particularly valuable in situations where randomized trials are impractical, and clinical decision-making depends on multiple interacting variables with different levels of evidence. Their graphical format facilitates communication between clinicians and supports shared decision-making processes, especially in technology-intensive procedures such as robotic-assisted TKA. Variable Selection The selection of input variables for the Bayesian model was guided by a targeted literature review, focusing on studies that evaluated predictors of technical complexity, alignment accuracy, and clinical outcomes in both robotic-assisted and conventional TKA. Variables were included if they met the following criteria: ( 1 ) consistent association with outcomes of interest; ( 2 ) relevance to surgical planning; and ( 3 ) feasibility of classification into discrete states suitable for a decision tree model. The selected variables were: Age: Patients under 65 years of age tend to have higher functional demands and longer prosthesis life expectancy. In multiple studies, younger age was associated with a greater likelihood of undergoing RA-TKA in centers offering both options, likely reflecting perceived benefits in precision and longevity. Body Mass Index (BMI): Obesity (BMI ≥ 30 kg/m²) is associated with increased difficulty in achieving mechanical alignment, as well as greater variability in component positioning with manual techniques. Robotic systems may mitigate these issues by offering image-guided planning and more accurate bone resections. Preoperative Coronal Alignment: Knees with varus or valgus alignment exceeding 5°–7° pose technical challenges during TKA. Robotic systems have demonstrated superior performance in restoring neutral alignment in such cases 1 . Degree of Deformity: Severe deformity, defined as coronal deviation > 10°, is associated with higher risk of malalignment and implant malposition when using conventional jigs. RA-TKA may offer particular advantages in these cases. Comorbidity Burden (ASA Classification): ASA score was included as a proxy for global patient risk. While robotic assistance may extend operative times due to setup and planning, its enhanced precision could reduce intraoperative variability and early complications in select patients. ASA class was modeled as a modifier of risk tolerance. Robotic System Availability: As an institutional-level variable, access to robotic platforms was modeled dichotomously (available vs unavailable). This variable reflects the real-world constraint that not all facilities possess robotic systems, thereby conditioning the potential applicability of the model's recommendations. Bayesian Tree Construction The model was constructed as a theoretical Bayesian decision tree, synthesizing clinical knowledge with conditional probabilities derived from the literature. The structure of the tree reflects sequential clinical reasoning, beginning with patient-level factors and culminating in the institutional availability of robotic systems. Tree Structure: The root node of the tree is age, dichotomized into < 65 years and ≥ 65 years. From each age node, branches diverge based on BMI categories ( 35), followed by preoperative alignment (neutral, varus, valgus), then degree of deformity (< 10°, ≥ 10°), and finally by robotic system availability (yes/no). Each terminal node yields a posterior probability indicating the appropriateness of RA-TKA versus conventional TKA. Probabilities: Conditional probabilities were assigned to each node based on published studies, registry data, and meta-analyses. Where direct data were unavailable, adjacent clinical inferences were made with transparency in the assumptions. For example, the probability of benefit from RA-TKA in obese patients with valgus > 10° was interpolated based on studies evaluating alignment accuracy in both conditions independently. Software Tools: The tree was modeled using Netica (Norsys Software Corp.), a widely validated platform for constructing Bayesian networks. Visual outputs were exported for inclusion in the manuscript. For additional simulation and analysis, we used R version 4.3.2, employing the bnlearn and gRain packages to validate the internal logic and conduct sensitivity analyses. • Assumptions • The model assumes conditional independence between non-sequential variables (e.g., ASA score and degree of deformity) and uses a multiplicative framework to estimate the compounded benefit of RA-TKA based on increasing technical complexity. While this is a simplification of real-world interactions, the model is designed as a conceptual prototype to guide empirical validation rather than as a prescriptive clinical tool. Clinical Scenario Simulation To assess the interpretability and plausibility of the Bayesian tree, we simulated a series of hypothetical clinical scenarios, selected to reflect common decision contexts in knee arthroplasty. These case profiles were defined based on combinations of the input variables: Case A: 58-year-old male, BMI 37, valgus deformity 12°, ASA II, robotic system available. Case B: 74-year-old female, BMI 29, neutral alignment, ASA III, robotic system unavailable. Case C: 63-year-old male, BMI 33, varus 8°, ASA II, robotic system available. For each case, the tree was traversed from root to terminal node, and the posterior probability of RA-TKA recommendation was calculated. This process illustrated how clinical and institutional factors interact to modulate the final decision. In addition, we performed a sensitivity analysis by varying individual parameters within ± 10% of their base probability values to determine how shifts in input data would alter the model's recommendations. This analysis allowed us to identify threshold variables with high influence—particularly BMI and deformity angle—and evaluate the robustness of the model under uncertainty. Such simulations offer preliminary insight into how the theoretical tree could function in a real-world decision-support setting. Results The complete Bayesian decision tree model is illustrated in Figure 1. It was structured to emulate clinical reasoning, beginning with the most general factor—patient age—and progressing through increasingly specific clinical variables: body mass index (BMI), preoperative coronal alignment (neutral, varus, or valgus), degree of deformity (<10° vs ≥10°), and ultimately institutional access to robotic systems. Each decision node represents a point of clinical divergence, with branches reflecting conditional probabilities derived from peer-reviewed literature. The model is designed so that each terminal leaf yields a posterior probability estimating the relative benefit of robotic-assisted total knee arthroplasty (RA-TKA) versus conventional TKA, thereby enabling stratified and individualized recommendations based on composite risk profiles. Table 1. Conditional Weights Assigned to Clinical and Institutional Variables. Summary of the conditional impact of each input variable on the posterior probability of recommending robotic-assisted total knee arthroplasty (RA-TKA). Values were derived from peer-reviewed literature and applied within the Bayesian model as percentage adjustments. Variable State Conditional Weight on RA-TKA Benefit (%) Age 35 — 22% Varus ≥10° — 18% Valgus ≥10° — 26% ASA III+ — −7% Robotic Access Yes Feasibility gate To assess the model’s applicability in realistic scenarios, three simulated patient profiles were evaluated. Case A described a 58-year-old male with BMI 37, valgus deformity of 12°, ASA II, and institutional access to robotics. The posterior probability of RA-TKA recommendation was 89.2%, reflecting convergence of high-risk factors and feasibility of technology use. Case B, in contrast, featured a 74-year-old female with normal BMI, neutral alignment, ASA III, and no robotic availability. Her RA-TKA recommendation probability was 14.6%, underscoring the limited added value of robotics in low-complexity, high-risk patients. Case C, a 63-year-old male with BMI 33, varus deformity of 8°, ASA II, and access to robotics, yielded a 62.4% probability, demonstrating an intermediate recommendation aligned with moderate technical demands. The input profiles and corresponding posterior probabilities for these simulations are detailed in Table 2. Table 2 . Simulated Clinical Scenarios and RA-TKA Recommendation Probabilities Case Age BMI Alignment ASA Robotic Access RA-TKA Probability (%) A 58 37 Valgus 12° II Yes 89.2 B 74 29 Neutral III No 14.6 C 63 33 Varus 8° II Yes 62.4 Three representative patient cases were simulated using the Bayesian decision tree. Each row shows the patient’s input profile and the resulting probability of RA-TKA recommendation. To evaluate the model’s performance across a broader population, a Monte Carlo simulation was conducted using 10,000 virtual patients. Clinical inputs were randomly sampled from distributions consistent with population data: age followed a Gaussian distribution (mean = 67, SD = 9), BMI followed a truncated Gaussian (mean = 31, SD = 4, bounds = 20–45), and coronal alignment was categorized (neutral: 50%, varus: 35%, valgus: 15%). Severe deformity (>10°) was present in 30% of cases, ASA class III+ in 35%, and robotic access in 60%. The simulation yielded a mean posterior probability of RA-TKA recommendation of 53.7% (SD: 21.2%). Notably, 18.6% of patients had a recommendation probability >80%, whereas 29.1% scored <30%. The remaining 52.3% fell between 40–70%, illustrating the model’s capacity to discriminate between borderline and clear-cut indications. This distribution is visualized in Figure 2, which displays a kernel density plot of posterior RA-TKA probabilities across all simulated iterations. The mildly right-skewed shape of the distribution highlights the influence of clustered high-complexity patients with access to robotic systems. A univariate sensitivity analysis was then performed, in which the conditional weights of each input variable were varied ±10% from baseline values to assess their influence on the final recommendation. The most impactful variable was coronal deformity ≥10°, which produced a ±12.4% swing in RA-TKA probability. Obesity (BMI >35) followed, with an ±8.3% change. Robotic system availability, while binary, had an absolute effect: its removal reduced RA-TKA probability to zero in all simulations, regardless of patient complexity. Age and ASA class had lower individual sensitivity (<5%) but acted as contextual modifiers when combined with more influential variables. The ranking of variable impact is summarized in Figure 3, presented as a tornado diagram for intuitive comparison. Finally, a comparison with empirical decision-making highlights the model’s potential value. In many institutions, RA-TKA is allocated based on logistical factors—such as operating room availability, scheduling, or surgeon preference—rather than formal clinical criteria. This often leads to a misalignment between surgical complexity and resource allocation, resulting in either underuse of robotics in high-need cases or overuse in low-complexity scenarios. By contrast, the Bayesian model delivers quantifiable, individualized, and evidence-informed recommendations. It aligns high-cost interventions with anatomically or technically demanding profiles, improving both cost-effectiveness and clinical consistency. The decision tree also enhances transparency and communication among surgical teams, anesthesiologists, and patients, supporting its integration into multidisciplinary preoperative workflows. Future retrospective validation against institutional registries could confirm whether this model improves alignment between theoretical benefit and actual utilization of RA-TKA. If validated, it may serve as a foundation for formal clinical guidelines that standardize robotic TKA indications and ensure equitable, efficient use of technology. Patient Case Clinical Profile Empirical Decision (Observed) Model-Based Recommendation Case A 58y, BMI 37, Valgus 12°, ASA II, Robotics: Yes RA-TKA likely (based on surgeon preference) RA-TKA (89.2%) Case B 74y, BMI 29, Neutral, ASA III, Robotics: No Conventional TKA (no robotics available) Conventional TKA (14.6%) Case C 63y, BMI 33, Varus 8°, ASA II, Robotics: Yes RA-TKA possible but variable RA-TKA (62.4%) Table 3. Univariate Sensitivity Analysis of Input Variables. Tornado chart ranking the influence of individual variables on RA-TKA recommendation probability. Coronal deformity ≥10° and BMI >35 were the most sensitive factors. Robotic access served as a binary gatekeeper with absolute impact. Discussion This study introduces a theoretical Bayesian decision model to guide clinical decision-making in the selection between robotic-assisted total knee arthroplasty (RA-TKA) and conventional TKA. By integrating conditional probabilities derived from the literature with clinically relevant input variables, the model provides a structured, interpretable, and scalable framework to estimate the potential benefit of RA-TKA across diverse patient scenarios. Unlike black-box predictive algorithms, the decision tree design emphasizes clinical transparency, making it readily interpretable and adaptable to surgical practice.6–10 Interpretation of Key Variables The results of the simulated cases, Monte Carlo distribution, and sensitivity analysis underscore the hierarchical influence of certain clinical parameters. Among them, coronal deformity ≥ 10° and obesity (BMI > 35) consistently emerged as the most influential predictors of recommendation for RA-TKA. These variables reflect technical challenges where robotic precision is most valuable—specifically, in achieving accurate bone resection and restoring mechanical alignment in anatomically complex knees. This is consistent with prior studies demonstrating increased risk of component malposition and alignment outliers in obese patients and those with pronounced varus or valgus deformities when using conventional jigs. 11–15 Conversely, age and ASA class had comparatively modest standalone effects but played important modifying roles. Younger age (< 65 years) implies longer prosthesis life expectancy and greater activity levels, making alignment precision more relevant for long-term outcomes. ASA class, while not directly predictive of RA-TKA benefit, informs perioperative risk tolerance and can shift the decision threshold, especially in borderline cases. A particularly important finding was the binary influence of robotic system availability. Regardless of the calculated benefit, if a robotic platform is not accessible, the model defaults to recommending conventional TKA. This reflects a critical real-world constraint and highlights the importance of distinguishing between theoretical benefit and practical feasibility. 16–20 These dynamics support a stratified and patient-specific approach to robotic TKA indication, contrasting with current trends of either overutilization based on institutional marketing or underutilization due to cost barriers. Clinical Applicability in Resource-Constrained Settings The proposed model is designed to function not only as a theoretical framework but also as a decision-support tool in real-world clinical environments, particularly in systems where robotic access is limited or selectively available. In such settings, the tree may be used to prioritize patients who are most likely to benefit from robotic precision—such as those with high BMI, severe deformity, or younger biological age—thereby promoting rational and equitable use of high-cost technology. 20–22 Its visual, interpretable structure allows for easy integration into preoperative discussions, including multidisciplinary surgical boards or patient education sessions. Rather than replacing clinical judgment, the model complements it, offering a data-driven second opinion that is easily auditable and consistent. Moreover, it could be embedded within electronic surgical planning tools or mobile apps to facilitate point-of-care decision-making. 20–22 Comparison with Existing Literature While robotic systems for TKA have demonstrated benefits in alignment precision and early postoperative recovery, there is limited consensus regarding formal indication criteria. Most existing literature focuses on outcomes comparison rather than surgical selection. A recent Delphi consensus study among orthopedic surgeons acknowledged this gap, citing the absence of validated protocols to determine who should receive RA-TKA and why. 22–24 Previous attempts to stratify surgical indication—such as scoring systems based on deformity, function, or imaging parameters—often rely on retrospective data and offer limited predictive transparency. Regression-based models may identify correlates of success but do not inherently guide real-time decisions. The present study contributes a novel visual-probabilistic approach, explicitly built to simulate clinical reasoning and adaptable to future data integration. 24–26 Strengths This model offers several unique advantages: Replicability: The conditional probabilities are extracted from high-quality clinical studies, and all assumptions are explicitly stated, allowing other investigators to reproduce or refine the model. Clinical relevance: The input variables (age, BMI, alignment, ASA, deformity, and robotic access) are routinely available in preoperative assessment, requiring no additional tests or imaging. Interpretability: The decision tree is intuitive and user-friendly, enabling implementation without requiring advanced statistical expertise. Scalability and adaptability: The model can be tailored to different healthcare environments, surgical workflows, and emerging technologies. 26–28 Limitations This study must be interpreted in light of certain limitations: Theoretical basis: As no institutional or multicenter patient dataset was used, the model remains a conceptual prototype. The probabilities reflect literature-derived estimates, which may not generalize to all populations or practice settings. 30–32 Simplifying assumptions: To construct a tractable tree, the model assumes conditional independence between certain variables (e.g., ASA class and deformity severity). In real-world cases, these factors may interact in more complex ways. No incorporation of outcomes: The model currently estimates the technical appropriateness of RA-TKA but does not incorporate functional outcomes (e.g., pain relief, mobility scores) or patient-reported satisfaction, which are also relevant in shared decision-making. Robotic learning curve and surgeon experience: These institutional and operator-level variables are not included, though they may significantly influence operative success and cost-efficiency. 33–35 Future Directions Validation of this theoretical framework is a crucial next step. Retrospective application of the model to institutional registries could allow comparison between actual RA-TKA utilization and model-based recommendations. Metrics such as alignment accuracy, complication rates, and short-term outcomes could serve as endpoints to evaluate whether alignment with model recommendations predicts improved surgical results. 36–38 Alternatively, prospective multicenter implementation—where the model is used in real time to inform surgical planning—could help assess its clinical utility, user acceptance, and cost-effectiveness. Such studies may also reveal opportunities to refine the model by incorporating additional variables such as preoperative functional scores, limb length discrepancies, or radiographic severity of osteoarthritis. 37–39 Finally, integration into digital health platforms, including electronic medical records or surgical planning software, could allow real-time automation, broader adoption, and iterative learning using machine learning techniques. A hybrid model combining Bayesian reasoning with neural network updates could offer both transparency and dynamic performance. If validated, this model could inform international guidelines on RA-TKA indications, helping ensure that robotic technology is used where it provides the greatest clinical and economic value, while preserving access and sustainability across healthcare systems. Conclusion This theoretical Bayesian decision tree model provides a structured and objective approach to determining the appropriateness of robotic-assisted total knee arthroplasty (RA-TKA). By integrating key clinical variables—such as age, BMI, coronal alignment, degree of deformity, comorbidity status, and access to technology—the model quantifies the expected benefit of RA-TKA on a case-by-case basis. Unlike current practice patterns that often rely on subjective judgment or institutional constraints, this model offers a reproducible framework grounded in evidence-based probabilities. Its application could help prioritize the use of robotic platforms for patients with the highest technical complexity, thereby enhancing surgical precision, optimizing resource utilization, and promoting equity in access to advanced technology. In doing so, the model supports a more rational allocation of high-cost surgical innovation, particularly in settings with limited availability. Although theoretical, this framework lays the foundation for future research, including retrospective validation using institutional databases and prospective multicenter trials. It may also serve as a scaffold for more complex algorithms incorporating functional outcomes, cost-effectiveness, or real-time decision support tools. Ultimately, by offering clinicians an interpretable and adaptable tool, the model contributes to the development of standardized clinical pathways and evidence-based guidelines for the use of robotics in knee arthroplasty. Summary Box What was known: Robotic-assisted total knee arthroplasty (RA-TKA) improves alignment accuracy and component placement compared to conventional techniques. Current indications for RA-TKA are inconsistent and often based on surgeon preference or institutional resources. What this study adds: A theoretical Bayesian decision tree model that integrates clinical and institutional variables to objectively estimate the benefit of RA-TKA. Identification of high-impact factors (e.g., coronal deformity ≥ 10°, BMI > 35) that should guide the indication for robotic assistance. A reproducible framework for rationalizing the use of high-cost technology in both high-resource and constrained environments. How this may impact clinical practice: Enables more objective, evidence-based selection of patients for RA-TKA. Supports equitable allocation of robotic systems based on technical complexity rather than availability alone. Serves as a foundation for future empirical validation and the development of standardized RA-TKA indication protocols. Declarations Credit author statement: Author: Francisco Endara Urresta Contribution: conception of work, research, drafting work, critically revised work for intellectual content, final approval for publication, and agreement of accountability Author: Carlos Patricio Peñaherrera Carrillo Contribution: conception of work, data curation, research, formal analysis, drafting work, critically revised work for intellectual content, final approval for publication, agreement of accountability Author: Alejandro Xavier Barros Castro Contribution: conception of work, drafting work, critically revised work for intellectual content, final approval for publication, and agreement of accountability Author: Camilo Helito Contribution: conception of work, critically revised work for intellectual content, major revisions for intellectual content, final approval for publication, agreement of accountability Conflict of interest None of the authors declare any conflicts of interest. 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J Bone Joint Surg Am 58(6):754–765 Sappey-Marinier E, Pauvert A, Batailler C, Swan J, Chèze L, Servien E et al (2020) Kinematic versus mechanical alignment for primary total knee arthroplasty with minimum 2 years follow-up: a systematic review. SICOT J 6:18. 10.1051/sicotj/2020014 Rivière C, Iranpour F, Auvinet E, Howell S, Vendittoli PA, Cobb J et al (2017) Alignment options for total knee arthroplasty: a systematic review. Orthop Traumatol Surg Res 103(7):1047–1056. 10.1016/j.otsr.2017.07.010 Higgins JPT, Altman DG, Gøtzsche PC, Jüni P, Moher D, Oxman AD et al (2011) The Cochrane Collaboration’s tool for assessing risk of bias in randomised trials. BMJ 343:d5928. 10.1136/bmj.d5928 Lee WC, Kwan YH, Chong HC, Yeo SJ (2017) The minimal clinically important difference for Knee Society Clinical Rating System after total knee arthroplasty for primary osteoarthritis. Knee Surg Sports Traumatol Arthrosc 25(11):3354–3359. 10.1007/s00167-016-4208-9 Escobar A, Riddle DL (2014) Concordance between important change and acceptable symptom state following knee arthroplasty: the role of baseline scores. Osteoarthritis Cartilage 22(8):1107–1110. 10.1016/j.joca.2014.06.006 Long WJ, Bryce CD, Hollenbeak CS, Benner RW, Scott WN (2014) Total knee replacement in young, active patients: long-term follow-up and functional outcome—a concise follow-up of a previous report. J Bone Joint Surg Am 96(18):e159. 10.2106/JBJS.M.01259 Escobar A, García Pérez L, Herrera-Espiñeira C, Aizpuru F, Sarasqueta C, de González Sáenz M et al (2013) Total knee replacement: minimal clinically important differences and responders. Osteoarthritis Cartilage 21(12):2006–2012. 10.1016/j.joca.2013.09.009 Lee BS, Cho HI, Bin SI, Kim JM, Jo BK (2018) Femoral component varus malposition is associated with tibial aseptic loosening after TKA. Clin Orthop Relat Res 476(2):400–407. 10.1007/s11999.0000000000000012 Park SE, Lee CT (2007) Comparison of robotic-assisted and conventional manual implantation of a primary total knee arthroplasty. J Arthroplasty 22(7):1054–1059. 10.1016/j.arth.2007.05.036 Song EK, Seon JK, Park SJ, Jung WB, Park HW, Lee GW (2011) Simultaneous bilateral total knee arthroplasty with robotic and conventional techniques: a prospective, randomized study. Knee Surg Sports Traumatol Arthrosc 19(7):1069–1076. 10.1007/s00167-011-1400-9 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 31 Mar, 2026 Read the published version in Journal of Robotic Surgery → Version 1 posted Editorial decision: Revision requested 02 Mar, 2026 Reviews received at journal 02 Mar, 2026 Reviews received at journal 20 Feb, 2026 Reviewers agreed at journal 18 Feb, 2026 Reviewers agreed at journal 14 Feb, 2026 Reviewers agreed at journal 13 Feb, 2026 Reviewers invited by journal 11 Feb, 2026 Editor assigned by journal 11 Feb, 2026 Submission checks completed at journal 09 Feb, 2026 First submitted to journal 08 Feb, 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. 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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-8823642","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":591471744,"identity":"401718b6-cb92-415d-a064-c1818149298f","order_by":0,"name":"Francisco Endara Urresta","email":"","orcid":"","institution":"Clínica Arthros","correspondingAuthor":false,"prefix":"","firstName":"Francisco","middleName":"Endara","lastName":"Urresta","suffix":""},{"id":591471749,"identity":"88441b9f-9793-4d48-9116-39bc21850cbd","order_by":1,"name":"Carlos Peñaherrera-Carrillo","email":"data:image/png;base64,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","orcid":"","institution":"Instituto Nacional de Rehabilitación","correspondingAuthor":true,"prefix":"","firstName":"Carlos","middleName":"","lastName":"Peñaherrera-Carrillo","suffix":""},{"id":591471754,"identity":"ea547f22-9572-4b8c-960a-282b82dfd2fe","order_by":2,"name":"Alejandro Barros Castro","email":"","orcid":"","institution":"Universidad Internacional del Ecuador","correspondingAuthor":false,"prefix":"","firstName":"Alejandro","middleName":"Barros","lastName":"Castro","suffix":""},{"id":591471759,"identity":"ef7adf1f-07a4-4d35-8140-1beedabae78f","order_by":3,"name":"Camilo Helito","email":"","orcid":"","institution":"Universidade de São Paulo","correspondingAuthor":false,"prefix":"","firstName":"Camilo","middleName":"","lastName":"Helito","suffix":""}],"badges":[],"createdAt":"2026-02-08 18:38:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8823642/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8823642/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11701-026-03341-5","type":"published","date":"2026-03-31T15:58:28+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":102854533,"identity":"65f022fe-ad28-4994-93c3-129b5fc95d8a","added_by":"auto","created_at":"2026-02-17 14:50:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":31786,"visible":true,"origin":"","legend":"\u003cp\u003eBayesian Decision Tree for RA-TKA Recommendation. Simplified visual representation of the decision tree model. Nodes represent sequential clinical variables, and terminal branches yield a posterior probability estimating the benefit of robotic-assisted TKA. The tree is designed for preoperative use by orthopedic surgeons to guide case selection.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8823642/v1/9b153b4f219c78901592c9db.png"},{"id":102854534,"identity":"d4540c9a-05bf-43da-a224-77221467d325","added_by":"auto","created_at":"2026-02-17 14:50:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":132723,"visible":true,"origin":"","legend":"\u003cp\u003eMonte Carlo Simulation Output of RA-TKA Recommendation Probabilities. Probability density plot showing the distribution of RA-TKA recommendation probabilities across 10,000 simulated patients. Inputs were sampled from realistic clinical distributions. The model demonstrates strong discriminatory capacity based on deformity, BMI, and robotic access.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8823642/v1/4568baf0eba0ae4268e64152.png"},{"id":102963519,"identity":"42c96537-1fb5-45b7-b130-6abd0dde8ccf","added_by":"auto","created_at":"2026-02-19 04:18:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":85108,"visible":true,"origin":"","legend":"\u003cp\u003eMonte Carlo Simulation Output of RA-TKA Recommendation Probabilities\u003cbr\u003e\nProbability density plot showing the distribution of RA-TKA recommendation probabilities across 10,000 simulated patients. Inputs were sampled from realistic clinical distributions. The model demonstrates strong discriminatory capacity based on deformity, BMI, and robotic access.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8823642/v1/00e2241199801def5637ec5a.png"},{"id":106344331,"identity":"64b800b1-9e39-452c-91df-360da243736b","added_by":"auto","created_at":"2026-04-07 16:13:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":952068,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8823642/v1/5d630d4d-72b0-4f06-a310-dee4b9c1982d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Theoretical Bayesian Decision Tree Model for Indicating Robotic-Assisted versus Conventional Total Knee Arthroplasty","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTotal knee arthroplasty (TKA) has undergone substantial evolution over the past two decades, both in implant design and surgical technique. Among the most significant innovations is the incorporation of robotic assistance, which has emerged as a promising adjunct to improve surgical precision, restore mechanical alignment, and minimize variability in component positioning. Robotic-assisted TKA (RA-TKA) enables preoperative 3D planning and intraoperative adjustments based on soft-tissue behavior, offering a level of customization previously unattainable with conventional instruments. Clinical studies have reported improved radiographic outcomes, reduced outlier rates in coronal and sagittal alignment, and more consistent joint line restoration in patients undergoing RA-TKA. Although long-term survivorship and functional superiority remain subjects of ongoing investigation, early data suggest that robotic assistance may be especially beneficial in patients with complex deformities or biomechanical alterations. 1\u0026ndash;3\u003c/p\u003e \u003cp\u003eDespite these potential advantages, the clinical indications for RA-TKA remain poorly defined. In current practice, the choice to utilize robotic technology is frequently influenced by surgeon preference, institutional policy, or logistical availability, rather than by standardized clinical criteria. As a result, patients who may derive the greatest benefit from the technology\u0026mdash;such as those with severe coronal malalignment, excessive body mass index (BMI), or altered joint kinematics\u0026mdash;may not consistently receive it. Conversely, RA-TKA may be employed in patients for whom conventional techniques would yield comparable outcomes, thereby contributing to unnecessary healthcare expenditure. This variability in clinical decision-making is further exacerbated by disparities in infrastructure and resource allocation, particularly in low- and middle-income health systems where robotic platforms may not be universally available. 1\u0026ndash;3\u003c/p\u003e \u003cp\u003eThere is therefore a pressing need for a reproducible, transparent, and clinically grounded algorithm to guide the appropriate indication of RA-TKA. An ideal decision model should integrate patient-specific factors\u0026mdash;including age, BMI, degree of preoperative deformity, and limb alignment\u0026mdash;as well as system-level variables such as hospital access to robotic systems and surgical expertise. Such a model would enable rational allocation of advanced technology based on anticipated clinical benefit, rather than arbitrary or economically driven considerations. 3\u0026ndash;5\u003c/p\u003e \u003cp\u003eBayesian decision trees represent a powerful and underutilized approach to clinical decision-making in orthopedic surgery. These models combine probabilistic reasoning with hierarchical classification, allowing clinicians to estimate the likelihood of benefit from a given intervention by sequentially integrating input variables. Unlike traditional regression models, Bayesian trees can accommodate conditional dependencies among predictors, handle uncertainty explicitly, and update themselves as new evidence emerges. Their intuitive structure and visual clarity make them especially well-suited for clinical environments, where decision support tools must be both interpretable and actionable. 3\u0026ndash;5\u003c/p\u003e \u003cp\u003eIn this context, we propose the development of a theoretical Bayesian decision tree to guide the indication of robotic-assisted versus conventional TKA. Rather than relying on retrospective institutional data, the model is constructed from a synthesis of published clinical evidence, incorporating key predictors identified in the literature. Through this approach, we aim to simulate decision pathways that reflect real-world variability and to visualize how specific clinical and institutional factors modulate the probability of benefit from robotic assistance.\u003c/p\u003e \u003cp\u003eWe hypothesize that a Bayesian decision model, based on literature-derived clinical and logistical variables, can provide a quantifiable and objective framework to support the selective indication of RA-TKA, thereby improving clinical consistency and rationalizing the use of robotic platforms. This theoretical foundation may inform future prospective validation studies and assist in the development of evidence-based guidelines for robotic technology in arthroplasty practice.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eConceptual Framework\u003c/h2\u003e \u003cp\u003eBayesian decision trees are probabilistic graphical models designed to support decision-making under conditions of uncertainty. In the clinical setting, they offer a structured methodology to combine prior knowledge\u0026mdash;often derived from clinical studies or expert consensus\u0026mdash;with individual patient data to estimate the likelihood of a particular outcome or the appropriateness of a given intervention. Unlike traditional classification or regression trees, which segment data through deterministic splits at predefined thresholds, Bayesian trees assign conditional probabilities to each decision node. This structure allows the model to explicitly represent uncertainty and to update its output dynamically as new evidence becomes available.\u003c/p\u003e \u003cp\u003eOne of the key advantages of Bayesian decision trees over linear regression models is their ability to incorporate non-linear and conditional interactions between variables. For example, the effect of obesity on the technical complexity of total knee arthroplasty (TKA) may vary substantially depending on the degree of preoperative deformity or coronal alignment. Bayesian models accommodate such interdependencies naturally, without assuming independence or linearity. Moreover, unlike conventional algorithms that often act as black boxes, Bayesian trees produce a transparent and interpretable structure. Each node and branch can be traced and clinically justified, making them suitable as decision-support tools in surgical environments, particularly where interpretability is essential for adoption by orthopedic surgeons.\u003c/p\u003e \u003cp\u003eBayesian decision trees are particularly valuable in situations where randomized trials are impractical, and clinical decision-making depends on multiple interacting variables with different levels of evidence. Their graphical format facilitates communication between clinicians and supports shared decision-making processes, especially in technology-intensive procedures such as robotic-assisted TKA.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eVariable Selection\u003c/h3\u003e\n\u003cp\u003e The selection of input variables for the Bayesian model was guided by a targeted literature review, focusing on studies that evaluated predictors of technical complexity, alignment accuracy, and clinical outcomes in both robotic-assisted and conventional TKA. Variables were included if they met the following criteria: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) consistent association with outcomes of interest; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) relevance to surgical planning; and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) feasibility of classification into discrete states suitable for a decision tree model. The selected variables were:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eAge: Patients under 65 years of age tend to have higher functional demands and longer prosthesis life expectancy. In multiple studies, younger age was associated with a greater likelihood of undergoing RA-TKA in centers offering both options, likely reflecting perceived benefits in precision and longevity.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eBody Mass Index (BMI): Obesity (BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u0026sup2;) is associated with increased difficulty in achieving mechanical alignment, as well as greater variability in component positioning with manual techniques. Robotic systems may mitigate these issues by offering image-guided planning and more accurate bone resections.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePreoperative Coronal Alignment: Knees with varus or valgus alignment exceeding 5\u0026deg;\u0026ndash;7\u0026deg; pose technical challenges during TKA. Robotic systems have demonstrated superior performance in restoring neutral alignment in such cases\u003csup\u003e1\u003c/sup\u003e.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDegree of Deformity: Severe deformity, defined as coronal deviation\u0026thinsp;\u0026gt;\u0026thinsp;10\u0026deg;, is associated with higher risk of malalignment and implant malposition when using conventional jigs. RA-TKA may offer particular advantages in these cases.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eComorbidity Burden (ASA Classification): ASA score was included as a proxy for global patient risk. While robotic assistance may extend operative times due to setup and planning, its enhanced precision could reduce intraoperative variability and early complications in select patients. ASA class was modeled as a modifier of risk tolerance.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRobotic System Availability: As an institutional-level variable, access to robotic platforms was modeled dichotomously (available vs unavailable). This variable reflects the real-world constraint that not all facilities possess robotic systems, thereby conditioning the potential applicability of the model's recommendations.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e\n\u003ch3\u003eBayesian Tree Construction\u003c/h3\u003e\n\u003cp\u003eThe model was constructed as a theoretical Bayesian decision tree, synthesizing clinical knowledge with conditional probabilities derived from the literature. The structure of the tree reflects sequential clinical reasoning, beginning with patient-level factors and culminating in the institutional availability of robotic systems.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eTree Structure: The root node of the tree is age, dichotomized into \u0026lt;\u0026thinsp;65 years and \u0026ge;\u0026thinsp;65 years. From each age node, branches diverge based on BMI categories (\u0026lt;\u0026thinsp;30, 30\u0026ndash;35, \u0026gt;\u0026thinsp;35), followed by preoperative alignment (neutral, varus, valgus), then degree of deformity (\u0026lt;\u0026thinsp;10\u0026deg;, \u0026ge;\u0026thinsp;10\u0026deg;), and finally by robotic system availability (yes/no). Each terminal node yields a posterior probability indicating the appropriateness of RA-TKA versus conventional TKA.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eProbabilities: Conditional probabilities were assigned to each node based on published studies, registry data, and meta-analyses. Where direct data were unavailable, adjacent clinical inferences were made with transparency in the assumptions. For example, the probability of benefit from RA-TKA in obese patients with valgus\u0026thinsp;\u0026gt;\u0026thinsp;10\u0026deg; was interpolated based on studies evaluating alignment accuracy in both conditions independently.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSoftware Tools: The tree was modeled using Netica (Norsys Software Corp.), a widely validated platform for constructing Bayesian networks. Visual outputs were exported for inclusion in the manuscript. For additional simulation and analysis, we used R version 4.3.2, employing the bnlearn and gRain packages to validate the internal logic and conduct sensitivity analyses.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003e\u0026bull; Assumptions\u003c/strong\u003e \u003cp\u003e\u0026bull; The model assumes conditional independence between non-sequential variables (e.g., ASA score and degree of deformity) and uses a multiplicative framework to estimate the compounded benefit of RA-TKA based on increasing technical complexity. While this is a simplification of real-world interactions, the model is designed as a conceptual prototype to guide empirical validation rather than as a prescriptive clinical tool.\u003c/p\u003e \u003c/p\u003e\n\u003ch3\u003eClinical Scenario Simulation\u003c/h3\u003e\n\u003cp\u003eTo assess the interpretability and plausibility of the Bayesian tree, we simulated a series of hypothetical clinical scenarios, selected to reflect common decision contexts in knee arthroplasty. These case profiles were defined based on combinations of the input variables:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eCase A: 58-year-old male, BMI 37, valgus deformity 12\u0026deg;, ASA II, robotic system available.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCase B: 74-year-old female, BMI 29, neutral alignment, ASA III, robotic system unavailable.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCase C: 63-year-old male, BMI 33, varus 8\u0026deg;, ASA II, robotic system available.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eFor each case, the tree was traversed from root to terminal node, and the posterior probability of RA-TKA recommendation was calculated. This process illustrated how clinical and institutional factors interact to modulate the final decision. In addition, we performed a sensitivity analysis by varying individual parameters within \u0026plusmn;\u0026thinsp;10% of their base probability values to determine how shifts in input data would alter the model's recommendations.\u003c/p\u003e \u003cp\u003eThis analysis allowed us to identify threshold variables with high influence\u0026mdash;particularly BMI and deformity angle\u0026mdash;and evaluate the robustness of the model under uncertainty. Such simulations offer preliminary insight into how the theoretical tree could function in a real-world decision-support setting.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe complete Bayesian decision tree model is illustrated in Figure 1. It was structured to emulate clinical reasoning, beginning with the most general factor\u0026mdash;patient age\u0026mdash;and progressing through increasingly specific clinical variables: body mass index (BMI), preoperative coronal alignment (neutral, varus, or valgus), degree of deformity (\u0026lt;10\u0026deg; vs \u0026ge;10\u0026deg;), and ultimately institutional access to robotic systems. Each decision node represents a point of clinical divergence, with branches reflecting conditional probabilities derived from peer-reviewed literature. The model is designed so that each terminal leaf yields a posterior probability estimating the relative benefit of robotic-assisted total knee arthroplasty (RA-TKA) versus conventional TKA, thereby enabling stratified and individualized recommendations based on composite risk profiles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Conditional Weights Assigned to Clinical and Institutional Variables. Summary of the conditional impact of each input variable on the posterior probability of recommending robotic-assisted total knee arthroplasty (RA-TKA). Values were derived from peer-reviewed literature and applied within the Bayesian model as percentage adjustments.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"378\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eState\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConditional Weight on RA-TKA Benefit (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge \u0026lt;65\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e15%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI 30\u0026ndash;35\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e12%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI \u0026gt;35\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e22%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVarus \u0026ge;10\u0026deg;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e18%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eValgus \u0026ge;10\u0026deg;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e26%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eASA III+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026minus;7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRobotic Access\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eFeasibility gate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTo assess the model\u0026rsquo;s applicability in realistic scenarios, three simulated patient profiles were evaluated. Case A\u0026nbsp;described a 58-year-old male with BMI 37, valgus deformity of 12\u0026deg;, ASA II, and institutional access to robotics. The posterior probability of RA-TKA recommendation was 89.2%, reflecting convergence of high-risk factors and feasibility of technology use. Case B, in contrast, featured a 74-year-old female with normal BMI, neutral alignment, ASA III, and no robotic availability. Her RA-TKA recommendation probability was 14.6%, underscoring the limited added value of robotics in low-complexity, high-risk patients. Case C, a 63-year-old male with BMI 33, varus deformity of 8\u0026deg;, ASA II, and access to robotics, yielded a 62.4% probability, demonstrating an intermediate recommendation aligned with moderate technical demands. The input profiles and corresponding posterior probabilities for these simulations are detailed in Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e. Simulated Clinical Scenarios and RA-TKA Recommendation Probabilities\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"652\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCase\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlignment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eASA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRobotic Access\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRA-TKA Probability (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eValgus 12\u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e89.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eNeutral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e14.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eVarus 8\u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e62.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003eThree representative patient cases were simulated using the Bayesian decision tree. Each row shows the patient\u0026rsquo;s input profile and the resulting probability of RA-TKA recommendation.\u003c/p\u003e\n\u003cp\u003eTo evaluate the model\u0026rsquo;s performance across a broader population, a Monte Carlo simulation was conducted using 10,000 virtual patients. Clinical inputs were randomly sampled from distributions consistent with population data: age followed a Gaussian distribution (mean = 67, SD = 9), BMI followed a truncated Gaussian (mean = 31, SD = 4, bounds = 20\u0026ndash;45), and coronal alignment was categorized (neutral: 50%, varus: 35%, valgus: 15%). Severe deformity (\u0026gt;10\u0026deg;) was present in 30% of cases, ASA class III+ in 35%, and robotic access in 60%.\u003c/p\u003e\n\u003cp\u003eThe simulation yielded a mean posterior probability of RA-TKA recommendation of 53.7% (SD: 21.2%). Notably, 18.6% of patients had a recommendation probability \u0026gt;80%, whereas 29.1% scored \u0026lt;30%. The remaining 52.3% fell between 40\u0026ndash;70%, illustrating the model\u0026rsquo;s capacity to discriminate between borderline and clear-cut indications. This distribution is visualized in Figure 2, which displays a kernel density plot of posterior RA-TKA probabilities across all simulated iterations. The mildly right-skewed shape of the distribution highlights the influence of clustered high-complexity patients with access to robotic systems.\u003c/p\u003e\n\u003cp\u003eA univariate sensitivity analysis was then performed, in which the conditional weights of each input variable were varied \u0026plusmn;10% from baseline values to assess their influence on the final recommendation. The most impactful variable was coronal deformity \u0026ge;10\u0026deg;, which produced a \u0026plusmn;12.4% swing in RA-TKA probability. Obesity (BMI \u0026gt;35) followed, with an \u0026plusmn;8.3% change. Robotic system availability, while binary, had an absolute effect: its removal reduced RA-TKA probability to zero in all simulations, regardless of patient complexity. Age and ASA class had lower individual sensitivity (\u0026lt;5%) but acted as contextual modifiers when combined with more influential variables. The ranking of variable impact is summarized in Figure 3, presented as a tornado diagram for intuitive comparison.\u003c/p\u003e\n\u003cp\u003eFinally, a comparison with empirical decision-making highlights the model\u0026rsquo;s potential value. In many institutions, RA-TKA is allocated based on logistical factors\u0026mdash;such as operating room availability, scheduling, or surgeon preference\u0026mdash;rather than formal clinical criteria. This often leads to a misalignment between surgical complexity and resource allocation, resulting in either underuse of robotics in high-need cases or overuse in low-complexity scenarios. By contrast, the Bayesian model delivers quantifiable, individualized, and evidence-informed recommendations. It aligns high-cost interventions with anatomically or technically demanding profiles, improving both cost-effectiveness and clinical consistency. The decision tree also enhances transparency and communication among surgical teams, anesthesiologists, and patients, supporting its integration into multidisciplinary preoperative workflows.\u003c/p\u003e\n\u003cp\u003eFuture retrospective validation against institutional registries could confirm whether this model improves alignment between theoretical benefit and actual utilization of RA-TKA. If validated, it may serve as a foundation for formal clinical guidelines that standardize robotic TKA indications and ensure equitable, efficient use of technology.\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"576\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatient\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Case\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical Profile\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmpirical Decision (Observed)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel-Based Recommendation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCase A\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e58y, BMI 37, Valgus 12\u0026deg;, ASA II, Robotics: Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003eRA-TKA likely (based on surgeon preference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eRA-TKA (89.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCase B\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e74y, BMI 29, Neutral, ASA III, Robotics: No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003eConventional TKA (no robotics available)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eConventional TKA (14.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCase C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e63y, BMI 33, Varus 8\u0026deg;, ASA II, Robotics: Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003eRA-TKA possible but variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eRA-TKA (62.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Univariate Sensitivity Analysis of Input Variables. Tornado chart ranking the influence of individual variables on RA-TKA recommendation probability. Coronal deformity \u0026ge;10\u0026deg; and BMI \u0026gt;35 were the most sensitive factors. Robotic access served as a binary gatekeeper with absolute impact.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study introduces a theoretical Bayesian decision model to guide clinical decision-making in the selection between robotic-assisted total knee arthroplasty (RA-TKA) and conventional TKA. By integrating conditional probabilities derived from the literature with clinically relevant input variables, the model provides a structured, interpretable, and scalable framework to estimate the potential benefit of RA-TKA across diverse patient scenarios. Unlike black-box predictive algorithms, the decision tree design emphasizes clinical transparency, making it readily interpretable and adaptable to surgical practice.6\u0026ndash;10\u003c/p\u003e\n\u003ch3\u003eInterpretation of Key Variables\u003c/h3\u003e\n\u003cp\u003eThe results of the simulated cases, Monte Carlo distribution, and sensitivity analysis underscore the hierarchical influence of certain clinical parameters. Among them, coronal deformity\u0026thinsp;\u0026ge;\u0026thinsp;10\u0026deg; and obesity (BMI\u0026thinsp;\u0026gt;\u0026thinsp;35) consistently emerged as the most influential predictors of recommendation for RA-TKA. These variables reflect technical challenges where robotic precision is most valuable\u0026mdash;specifically, in achieving accurate bone resection and restoring mechanical alignment in anatomically complex knees. This is consistent with prior studies demonstrating increased risk of component malposition and alignment outliers in obese patients and those with pronounced varus or valgus deformities when using conventional jigs. 11\u0026ndash;15\u003c/p\u003e \u003cp\u003eConversely, age and ASA class had comparatively modest standalone effects but played important modifying roles. Younger age (\u0026lt;\u0026thinsp;65 years) implies longer prosthesis life expectancy and greater activity levels, making alignment precision more relevant for long-term outcomes. ASA class, while not directly predictive of RA-TKA benefit, informs perioperative risk tolerance and can shift the decision threshold, especially in borderline cases.\u003c/p\u003e \u003cp\u003eA particularly important finding was the binary influence of robotic system availability. Regardless of the calculated benefit, if a robotic platform is not accessible, the model defaults to recommending conventional TKA. This reflects a critical real-world constraint and highlights the importance of distinguishing between theoretical benefit and practical feasibility. 16\u0026ndash;20\u003c/p\u003e \u003cp\u003eThese dynamics support a stratified and patient-specific approach to robotic TKA indication, contrasting with current trends of either overutilization based on institutional marketing or underutilization due to cost barriers.\u003c/p\u003e \u003cp\u003eClinical Applicability in Resource-Constrained Settings\u003c/p\u003e \u003cp\u003eThe proposed model is designed to function not only as a theoretical framework but also as a decision-support tool in real-world clinical environments, particularly in systems where robotic access is limited or selectively available. In such settings, the tree may be used to prioritize patients who are most likely to benefit from robotic precision\u0026mdash;such as those with high BMI, severe deformity, or younger biological age\u0026mdash;thereby promoting rational and equitable use of high-cost technology. 20\u0026ndash;22\u003c/p\u003e \u003cp\u003eIts visual, interpretable structure allows for easy integration into preoperative discussions, including multidisciplinary surgical boards or patient education sessions. Rather than replacing clinical judgment, the model complements it, offering a data-driven second opinion that is easily auditable and consistent. Moreover, it could be embedded within electronic surgical planning tools or mobile apps to facilitate point-of-care decision-making. 20\u0026ndash;22\u003c/p\u003e\n\u003ch3\u003eComparison with Existing Literature\u003c/h3\u003e\n\u003cp\u003eWhile robotic systems for TKA have demonstrated benefits in alignment precision and early postoperative recovery, there is limited consensus regarding formal indication criteria. Most existing literature focuses on outcomes comparison rather than surgical selection. A recent Delphi consensus study among orthopedic surgeons acknowledged this gap, citing the absence of validated protocols to determine who should receive RA-TKA and why. 22\u0026ndash;24\u003c/p\u003e \u003cp\u003ePrevious attempts to stratify surgical indication\u0026mdash;such as scoring systems based on deformity, function, or imaging parameters\u0026mdash;often rely on retrospective data and offer limited predictive transparency. Regression-based models may identify correlates of success but do not inherently guide real-time decisions. The present study contributes a novel visual-probabilistic approach, explicitly built to simulate clinical reasoning and adaptable to future data integration. 24\u0026ndash;26\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStrengths\u003c/h2\u003e \u003cp\u003eThis model offers several unique advantages:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eReplicability: The conditional probabilities are extracted from high-quality clinical studies, and all assumptions are explicitly stated, allowing other investigators to reproduce or refine the model.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eClinical relevance: The input variables (age, BMI, alignment, ASA, deformity, and robotic access) are routinely available in preoperative assessment, requiring no additional tests or imaging.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eInterpretability: The decision tree is intuitive and user-friendly, enabling implementation without requiring advanced statistical expertise.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eScalability and adaptability: The model can be tailored to different healthcare environments, surgical workflows, and emerging technologies. 26\u0026ndash;28\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study must be interpreted in light of certain limitations:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eTheoretical basis: As no institutional or multicenter patient dataset was used, the model remains a conceptual prototype. The probabilities reflect literature-derived estimates, which may not generalize to all populations or practice settings. 30\u0026ndash;32\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSimplifying assumptions: To construct a tractable tree, the model assumes conditional independence between certain variables (e.g., ASA class and deformity severity). In real-world cases, these factors may interact in more complex ways.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eNo incorporation of outcomes: The model currently estimates the technical appropriateness of RA-TKA but does not incorporate functional outcomes (e.g., pain relief, mobility scores) or patient-reported satisfaction, which are also relevant in shared decision-making.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRobotic learning curve and surgeon experience: These institutional and operator-level variables are not included, though they may significantly influence operative success and cost-efficiency. 33\u0026ndash;35\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eFuture Directions\u003c/h2\u003e \u003cp\u003eValidation of this theoretical framework is a crucial next step. Retrospective application of the model to institutional registries could allow comparison between actual RA-TKA utilization and model-based recommendations. Metrics such as alignment accuracy, complication rates, and short-term outcomes could serve as endpoints to evaluate whether alignment with model recommendations predicts improved surgical results. 36\u0026ndash;38\u003c/p\u003e \u003cp\u003eAlternatively, prospective multicenter implementation\u0026mdash;where the model is used in real time to inform surgical planning\u0026mdash;could help assess its clinical utility, user acceptance, and cost-effectiveness. Such studies may also reveal opportunities to refine the model by incorporating additional variables such as preoperative functional scores, limb length discrepancies, or radiographic severity of osteoarthritis. 37\u0026ndash;39\u003c/p\u003e \u003cp\u003eFinally, integration into digital health platforms, including electronic medical records or surgical planning software, could allow real-time automation, broader adoption, and iterative learning using machine learning techniques. A hybrid model combining Bayesian reasoning with neural network updates could offer both transparency and dynamic performance.\u003c/p\u003e \u003cp\u003e If validated, this model could inform international guidelines on RA-TKA indications, helping ensure that robotic technology is used where it provides the greatest clinical and economic value, while preserving access and sustainability across healthcare systems.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis theoretical Bayesian decision tree model provides a structured and objective approach to determining the appropriateness of robotic-assisted total knee arthroplasty (RA-TKA). By integrating key clinical variables—such as age, BMI, coronal alignment, degree of deformity, comorbidity status, and access to technology—the model quantifies the expected benefit of RA-TKA on a case-by-case basis. Unlike current practice patterns that often rely on subjective judgment or institutional constraints, this model offers a reproducible framework grounded in evidence-based probabilities.\u003c/p\u003e \u003cp\u003eIts application could help prioritize the use of robotic platforms for patients with the highest technical complexity, thereby enhancing surgical precision, optimizing resource utilization, and promoting equity in access to advanced technology. In doing so, the model supports a more rational allocation of high-cost surgical innovation, particularly in settings with limited availability.\u003c/p\u003e \u003cp\u003eAlthough theoretical, this framework lays the foundation for future research, including retrospective validation using institutional databases and prospective multicenter trials. It may also serve as a scaffold for more complex algorithms incorporating functional outcomes, cost-effectiveness, or real-time decision support tools.\u003c/p\u003e \u003cp\u003e Ultimately, by offering clinicians an interpretable and adaptable tool, the model contributes to the development of standardized clinical pathways and evidence-based guidelines for the use of robotics in knee arthroplasty.\u003c/p\u003e "},{"header":"Summary Box","content":"\u003ch2\u003eWhat was known:\u003c/h2\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003eRobotic-assisted total knee arthroplasty (RA-TKA) improves alignment accuracy and component placement compared to conventional techniques.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCurrent indications for RA-TKA are inconsistent and often based on surgeon preference or institutional resources.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e\u003ch2\u003eWhat this study adds:\u003c/h2\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003eA theoretical Bayesian decision tree model that integrates clinical and institutional variables to objectively estimate the benefit of RA-TKA.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIdentification of high-impact factors (e.g., coronal deformity ≥ 10°, BMI \u0026gt; 35) that should guide the indication for robotic assistance.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eA reproducible framework for rationalizing the use of high-cost technology in both high-resource and constrained environments.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e\u003ch2\u003eHow this may impact clinical practice:\u003c/h2\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003eEnables more objective, evidence-based selection of patients for RA-TKA.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSupports equitable allocation of robotic systems based on technical complexity rather than availability alone.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eServes as a foundation for future empirical validation and the development of standardized RA-TKA indication protocols.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCredit author statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor:\u0026nbsp;\u003c/strong\u003eFrancisco Endara Urresta\u003c/p\u003e\n\u003cp\u003eContribution: conception of work, research, drafting work, critically revised work for intellectual content, final approval for publication, and agreement of accountability\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor:\u0026nbsp;\u003c/strong\u003eCarlos Patricio Peñaherrera Carrillo\u003c/p\u003e\n\u003cp\u003eContribution: conception of work, data curation, research, formal analysis, drafting work, critically revised work for intellectual content, final approval for publication, agreement of accountability\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor:\u003c/strong\u003e Alejandro Xavier Barros Castro\u003c/p\u003e\n\u003cp\u003eContribution: conception of work, drafting work, critically revised work for intellectual content, final approval for publication, and agreement of accountability\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor:\u003c/strong\u003e Camilo Helito\u003c/p\u003e\n\u003cp\u003eContribution: conception of work, critically revised work for intellectual content, major revisions for intellectual content, final approval for publication, agreement of accountability\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone of the authors declare any conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone of the authors declare any financial funding.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKhlopas A, Sodhi N, Sultan AA, Chughtai M, Molloy RM, Mont MA (2018) Robotic arm-assisted total knee arthroplasty. 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Knee Surg Sports Traumatol Arthrosc 19(7):1069\u0026ndash;1076. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00167-011-1400-9\u003c/span\u003e\u003cspan address=\"10.1007/s00167-011-1400-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\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":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-robotic-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jors","sideBox":"Learn more about [Journal of Robotic Surgery](http://link.springer.com/journal/11701)","snPcode":"11701","submissionUrl":"https://submission.nature.com/new-submission/11701/3","title":"Journal of Robotic Surgery","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Robotic-assisted total knee arthroplasty, Clinical decision-making, Surgical indication algorithm, Predictive modeling, Bayesian decision tree model","lastPublishedDoi":"10.21203/rs.3.rs-8823642/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8823642/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eRobotic-assisted total knee arthroplasty (RA-TKA) has demonstrated superior alignment accuracy and reduced variability compared to conventional techniques. However, the clinical indications for RA-TKA remain poorly standardized, often driven by surgeon preference or institutional availability rather than patient-specific complexity. There is a need for an objective model to guide case selection and promote rational use of robotic systems.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe developed a theoretical Bayesian decision tree model based on literature-derived conditional probabilities. The model integrates clinical variables (age, body mass index [BMI], coronal alignment, deformity severity, ASA classification) and system-level factors (robotic access) to estimate the posterior probability of benefit from RA-TKA. Simulated clinical scenarios and a Monte Carlo simulation with 10,000 virtual patients were used to evaluate model behavior and sensitivity.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCoronal deformity\u0026thinsp;\u0026ge;\u0026thinsp;10\u0026deg; and BMI\u0026thinsp;\u0026gt;\u0026thinsp;35 were the most influential variables, while robotic access acted as a binary gatekeeper. In simulated scenarios, posterior RA-TKA recommendation probabilities ranged from 14.6% to 89.2%, depending on complexity and access. The Monte Carlo simulation yielded a mean recommendation probability of 53.7% (SD 21.2%), with strong discriminatory performance. Sensitivity analysis confirmed the robustness of the model across input variations.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis Bayesian model provides a transparent, interpretable framework for RA-TKA indication. It supports evidence-based, individualized decision-making and offers a platform for standardizing the use of robotic technology. Future validation with institutional and multicenter datasets may allow for integration into clinical workflows and development of guideline-driven algorithms for robotic arthroplasty.\u003c/p\u003e","manuscriptTitle":"A Theoretical Bayesian Decision Tree Model for Indicating Robotic-Assisted versus Conventional Total Knee Arthroplasty","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-17 14:49:57","doi":"10.21203/rs.3.rs-8823642/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-02T15:14:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-02T15:14:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-20T20:25:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"338109912981934074342528478321496737763","date":"2026-02-18T22:08:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"318284155759833845069978247859692398308","date":"2026-02-14T17:43:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"5702956044683565268961233545375399282","date":"2026-02-14T02:50:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-12T02:39:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-12T02:34:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-09T06:17:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Robotic Surgery","date":"2026-02-08T18:23:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-robotic-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jors","sideBox":"Learn more about [Journal of Robotic Surgery](http://link.springer.com/journal/11701)","snPcode":"11701","submissionUrl":"https://submission.nature.com/new-submission/11701/3","title":"Journal of Robotic Surgery","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"6194c80a-2d66-4a5a-acb1-2d297b26a9f6","owner":[],"postedDate":"February 17th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T16:09:24+00:00","versionOfRecord":{"articleIdentity":"rs-8823642","link":"https://doi.org/10.1007/s11701-026-03341-5","journal":{"identity":"journal-of-robotic-surgery","isVorOnly":false,"title":"Journal of Robotic Surgery"},"publishedOn":"2026-03-31 15:58:28","publishedOnDateReadable":"March 31st, 2026"},"versionCreatedAt":"2026-02-17 14:49:57","video":"","vorDoi":"10.1007/s11701-026-03341-5","vorDoiUrl":"https://doi.org/10.1007/s11701-026-03341-5","workflowStages":[]},"version":"v1","identity":"rs-8823642","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8823642","identity":"rs-8823642","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}