A Patient Digital Twin in Orthopedics. A Framework for Patient-Specific Care

The emergence of patient-specific digital twins is reshaping orthopedic practice, driven by progress in 3D reconstruction techniques and motion capture (MoCap) systems. This work proposes a comprehensive framework for developing digital twin models aimed at enhancing prevention, diagnosis, treatment, and long-term management of musculoskeletal disorders. The proposed approach integrates clinical expertise, insights from existing literature, and data derived from advanced computational technologies. Central to this framework is the application of 3D modeling for constructing accurate patient representations. In particular, deep learning methods enable automated segmentation of medical images, facilitating the generation of detailed three-dimensional anatomical models. These models support in-depth evaluation of individual morphology, which is critical for detecting abnormalities and tailoring therapeutic strategies. In addition, statistical shape modeling (SSM) is employed to investigate anatomical variability and pathological deviations across populations. This technique also contributes to automating the identification of anatomical landmarks and performing quantitative measurements, thereby simplifying the design of patient-specific prosthetic solutions. Finite Element Analysis (FEA) further complements the framework by simulating biomechanical behavior under both healthy and pathological conditions, offering predictive insights into treatment outcomes. Motion capture technology plays a key role in enriching digital twin models by enabling precise analysis of human movement. Such analyses are essential for detecting functional impairments, assessing surgical and rehabilitation outcomes, and tracking disease progression over time. The resulting digital twin system supports a wide range of clinical applications, including risk assessment, diagnosis, treatment planning, and post-intervention evaluation. The methodology is designed to be flexible and applicable across multiple orthopedic scenarios, addressing a variety of conditions while maintaining clarity and robustness. Its effectiveness is demonstrated through selected case studies representing common and clinically relevant pathologies affecting different anatomical regions. Among the main contributions of this work are the automated segmentation of knee structures, particularly the femur, using deep learning approaches, and the development of a statistical shape model focused on femoral morphology in anterior cruciate ligament (ACL) injury cases. Clinical applications include the analysis of morphological risk factors associated with knee ligament injuries and shoulder instability, as well as the identification of gait abnormalities such as Trendelenburg patterns through motion capture data. Finite element simulations are utilized to examine the biomechanical consequences of meniscal damage, while personalized treatment strategies are explored through the design of customized knee implants. Furthermore, gait analysis is applied to evaluate the effects of total hip arthroplasty and different surgical techniques. Overall, this study contributes to advancing digital twin technologies in orthopedics by presenting a versatile and scalable framework that supports more precise, individualized patient care.