Framework of a multiscale data-driven DT of the musculoskeletal system
This work addresses the need for advanced diagnostic tools for clinicians and researchers dealing with musculoskeletal disorders, though it appears incremental as it builds on existing digital twin and data integration paradigms.
The paper tackled the problem of managing musculoskeletal disorders by introducing the Musculoskeletal Digital Twin (MS-DT) framework, which integrates multiscale biomechanical data to create patient-specific models, resulting in precise extraction of kinematic and dynamic tissue features for improved diagnosis and intervention planning.
Musculoskeletal disorders (MSDs) are a leading cause of disability worldwide, requiring advanced diagnostic and therapeutic tools for personalised assessment and treatment. Effective management of MSDs involves the interaction of heterogeneous data sources, making the Digital Twin (DT) paradigm a valuable option. This paper introduces the Musculoskeletal Digital Twin (MS-DT), a novel framework that integrates multiscale biomechanical data with computational modelling to create a detailed, patient-specific representation of the musculoskeletal system. By combining motion capture, ultrasound imaging, electromyography, and medical imaging, the MS-DT enables the analysis of spinal kinematics, posture, and muscle function. An interactive visualisation platform provides clinicians and researchers with an intuitive interface for exploring biomechanical parameters and tracking patient-specific changes. Results demonstrate the effectiveness of MS-DT in extracting precise kinematic and dynamic tissue features, offering a comprehensive tool for monitoring spine biomechanics and rehabilitation. This framework provides high-fidelity modelling and real-time visualization to improve patient-specific diagnosis and intervention planning.