A FULLY AUTOMATED FRAMEWORK FOR DEVELOPING SHAPE-SPECIFIC MUSCULOSKELETAL MODELS

The mathematical representation of the skeleton and musculature and the estimation of muscle and joint articular forces are made feasible with computer simulations, eliminating the need for surgically invasive procedures. However, the clinical application of these models has been limited by concerns regarding the accessibility and accuracy of the modeling process. While generic musculoskeletal models can be scaled to approximate patient anatomy, they often fail to capture the unique geometric and structural characteristics of individual bones. The shape of the skeleton is particularly critical in musculoskeletal modeling, as it directly influences joint alignment, muscle attachment sites, and the mechanical behavior of the system during movement. The current research introduces an automated and open-source framework for creating shape-specific musculoskeletal models using OpenSim software. This framework integrates shape-specific skeletal geometries derived from CT scans, aligning and scaling the Rajagopal model to match individual anatomies as an initial step. Subsequently, it updates key parameters, including bone shape, muscle attachments, and joint centers. Additionally, muscle properties such as optimal fiber length, tendon slack length, and maximum isometric force are scaled to ensure physiological relevance. The proposed approach has been applied to 1000 patient datasets, enabling the prediction of joint reaction forces and muscle forces during a generic gait. While the framework currently uses generalized inputs such as ground reaction forces and gait patterns, it provides a foundational step toward more accurate, fast, and efficient musculoskeletal modeling. Preliminary results demonstrate that this framework can effectively capture biomechanical variations between individuals, offering a more precise and clinically relevant representation of musculoskeletal function. By providing a patient-specific alternative to generic scaling techniques, this framework addresses key limitations in current musculoskeletal modeling practices. Its open-source implementation enhances accessibility, paving the way for broader adoption and future refinements in clinical and research settings.