Yuyang Han

In the field of orthopedic surgery, particularly distraction osteogenesis (DO), the mechanical environment plays a decisive role in the quality of bone regeneration and the safety of the soft tissue envelope. The continuous monitoring and accurate prediction of distraction resisting forces (DRF) are critical for preventing soft tissue complications such as nerve ischemia, joint contractures, and mechanical failure of the lengthening device. However, current clinical practice relies heavily on subjective assessment or passive monitoring tools that lack predictive capabilities. To address this gap, this study proposes a comprehensive solution combining a custom mechanical acquisition system with a high-fidelity finite element (FE) prediction method. The system design features a novel “double-ring” sensor interface specifically engineered to decouple axial distraction forces from parasitic bending moments generated by asymmetric muscle tension. Furthermore, a patient-specific FE model utilizing the Ogden hyperelastic constitutive law was derived, explicitly based on the patient’s muscle volume from preoperative CT imaging, to predict the non-linear force evolution. The feasibility and accuracy of the system were validated in a pilot in vivo study using a single ovine model ($N=1$). To isolate the soft tissue resistance from callus formation, distraction was performed immediately postoperatively up to a total length of 4 cm. Experimental results demonstrated the system’s high linearity ($R^2>0.999$) and its ability to capture the characteristic viscoelastic relaxation of living tissues. The FE model successfully predicted the peak distraction forces, showing improved agreement with experimental data at larger distraction magnitudes. By integrating mechanical sensing with predictive modeling, this framework lays the foundation for future closed-loop, patient-specific control in distraction osteogenesis.