Mechanics map of living muscles: Emerging technologies and clinical applications
Living muscle is a dynamic biomechanical system whose functions are inherently tied to its mechanical properties, from fiber stiffness and viscoelasticity to tissue-level anisotropy. Alterations in these properties precede or accompany pathologies ranging from muscular dystrophies and sarcopenia to sports-related injuries, while physiological adaptations (e.g., training-induced hypertrophy) manifest as quantifiable mechanical shifts. For decades, muscle mechanics were studied ex vivo or via indirect in vivo measures (e.g., force production), limiting translation to clinical care. In recent years, mechanical mapping, the noninvasive, quantitative visualization of muscle biomechanical properties, has emerged as a transformative approach to bridge preclinical research and precision medicine. This commentary addresses recent technological advances, clinical applications, and unresolved challenges, outlining a roadmap for realizing the full potential of muscle mechanical mapping. Figure 1 illustrates the organization of this commentary.
Methods to establish the mechanics map of living muscles: From elastography to machine learning integration
Mechanical mapping relies on advances in imaging modalities that quantify tissue responses to mechanical or acoustic stimuli.3 Ultrasound elastography (USE), including shear wave elastography (SWE) and strain elastography, has become a useful tool for clinical muscle imaging, offering real-time, cost-effective mechanical mapping.4 Recent innovations in SWE, such as programmable mechanical excitation for shear wave generation, have improved signal-to-noise ratios in deep or heterogeneous muscles (e.g., the gastrocnemius or paravertebral muscles). As a complement to USE, magnetic resonance elastography (MRE) offers superior soft-tissue contrast, high spatial resolution, and three-dimensional mechanical mapping capabilities, making it well suited for assessing deep muscle groups (e.g., pelvic floor muscles) and detecting early pathological changes. However, the high cost and limited availability of MRE restrict its routine clinical use.
The next frontier in mechanical mapping lies in data integration with appropriate mechanical models and interpretation via machine learning (ML). Conventional mechanical models often treat muscle as a passive material, neglecting its intrinsically active and contractile nature. Active constitutive models are therefore needed to infer muscle mechanical properties accurately and broaden the applications of mechanical mapping to motion monitoring and health assessment.1 ML algorithms, including support vector machine (SVM), random forest (RF), convolutional neural network (CNN), and physics-informed neural network (PINN), have been used to classify muscle diseases, segment muscle tissues, correct motion artifacts, and fuse multimodal data to generate comprehensive mechanical-functional maps. For example, physics-informed methods such as SWENet have been developed to map tissue mechanical properties directly from experimental data.2 Meanwhile, an ML framework integrating SWE with B-mode ultrasound videos may support longitudinal trend analysis, monitoring of disease progression, and assessment of therapeutic response. These advances address a long-standing challenge of mechanical mapping: translating raw stiffness data into clinically actionable insights. More recently, transformer-based architectures, self-supervised learning, and large-scale foundation models have begun to reshape medical image analysis. Large language models (LLMs) and LLM-powered agents may facilitate the integration of functional assessments, longitudinal medical records, structural imaging, and elastography-derived mechanical information. They may further enable interactive clinical consultation, clinical decision support, and automated report generation, thereby facilitating the clinical translation of muscle mechanical mapping.
Clinical translation: From diagnosis to therapeutic monitoring
Mechanical mapping has already demonstrated clinical utility across diverse domains. In sports medicine, SWE has been used to characterize hamstring stiffness in athletes and monitor recovery from exercise-induced muscle fatigue. In geriatrics, mechanical mapping provides objective information on muscle quality and stiffness that are associated with sarcopenia, physical performance, and fall risk. In the field of neuromuscular disorders, particularly in muscular dystrophies, MRE has been shown to detect mechanical abnormalities not captured by conventional structural MRI, enabling early intervention.
Tissue engineering and regenerative medicine also stand to benefit. Mechanical mapping can noninvasively assess the functional maturity of engineered muscle constructs, both in vitro and in vivo, by quantifying stiffness and contractile responsiveness. In rehabilitation, elastography can quantify differences in muscle stiffness between paretic and nonparetic muscles and detect treatment-related changes, making it a promising tool for characterizing spasticity and monitoring therapeutic response.
