FROM SKELETON TO STRESS: A SCALABLE APPROACH FOR PERSONALIZED JOINT LOADING ANALYSIS

Direct measurement of intra-articular contact forces in the native knee joint presents significant ethical and practical challenges. Consequently, computational musculoskeletal modeling has emerged as a non-invasive tool for estimating joint loading. To avoid the laborious task of manual segmentation for model development, we developed a novel, scalable computational approach for generating patient-specific joint anatomy for the major joints of the lower limb (eg. The hip joint, the knee joint and the ankle joint). A personalized algorithm, utilizing statistical shape models (SSMs), was developed to predict soft tissue geometry of the hip, knee, and ankle joints based solely on skeletal anatomy. This approach leverages Magnetic Resonance Imaging (MRI) data from 53 healthy individuals for the identification of soft tissue anatomy and landmarks. Three distinct topographic cartilage thickness maps were generated, for cartilage modeling in the hip, knee and ankle joint. Elastic mesh wrapping was applied for ligamentous modeling. Specifically for the knee joint, meniscal modeling relied on wrapping a triangular geometry with varying height and width from the anterior to the posterior root. These personalized anatomical models were utilized as input for discrete element analysis (DEA), a computational method within the field of musculoskeletal modeling, enabling non-invasive estimation of joint loading conditions. Due to its computational simplicity, DEA offers a promising alternative to finite element analysis (FEA). The DEA models were developed as systems of linear springs representing cartilage-bone interfaces, and meniscus-cartilage-bone interfaces in the knee joint. Every spring received a spring stiffness, based on the specific material properties and the soft tissue thickness. The models were iteratively updated to balance the applied joint reaction forces, based on half the body weight, and the forces exerted by the springs. This research has strong clinical translation potential as it can inform surgical planning and treatment for various conditions. Specifically, knee DEA guides osteotomy planning, hip DEA analyzes impingement loading and surgical interventions, and ankle DEA models syndesmotic lesion effects on joint stress.