par Stenti, Andrea ;Morales Orcajo, Enrique;Innocenti, Bernardo
Référence Journal of Mechanics in Medicine and Biology, page (23), 2540113
Publication Publié, 2026-07-01
Article révisé par les pairs
Résumé : Background: The successful outcome of total joint arthroplasty depends on the interaction of several critical factors: rigorous, long-term, well-designed randomized controlled trials; the surgeon’s risk-informed decisions regarding prosthesis design, sizing, and alignment; and the selection of an optimal surgical approach tailored to the individual patient. Although recent advancements have improved clinical outcomes, data from national registries and meta-analyses indicate a persistent need for innovative techniques and strategies to ensure long-term success in joint arthroplasty. In light of these challenges, this study aims to demonstrate the potential of in-silico methods to address current limitations and to support evidence-based, patient-specific decision-making in orthopedic surgery. Methods: This study presents an in-silico toolbox that leverages Computer Modeling and Simulations (CMS) to evaluate the performance and safety of orthopedic medical devices. The toolbox allows for the assessment of novel prosthesis designs across virtual patient cohorts and supports the simulation of surgical risks using digital twins. To illustrate the functionality of the toolbox, a Total Ankle Replacement (TAR) scenario is employed as a representative case study. Several Key Performance Indicators (KPIs) are defined to quantify device performance and patient-specific outcomes. Additionally, a composite metric — the Prosthesis Quality Index (PQI) — is introduced, which integrates the selected KPIs into a single, interpretable measure to support clinical decision-making. Results: While individual KPIs provide valuable insights to healthcare professionals, they only offer a fragmented view that can hinder efficient risk analysis. In contrast, the PQI significantly enhances the risk-informed decision-making process by consolidating critical information. The PQI holistically incorporates both patient-specific factors (e.g., pain sensitivity, anatomical variability) and implant-related performance metrics (e.g., polyethylene wear). As a result, the PQI enables a more comprehensive and clinically meaningful evaluation of treatment options. Conclusion: This work represents a foundational step toward the broader integration of in-silico tools in orthopedic care. The proposed toolbox provides a scalable and adaptable framework for the evaluation of medical devices, enabling more efficient and personalized assessments. By supporting improved decision-making, this approach has the potential to enhance patient safety and treatment effectiveness across a wide range of joint replacement procedures.

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